import torch
import torch.nn as nn
from torch.hub import load_state_dict_from_url
from utils import NormalizeByChannelMeanStd
from pdb import set_trace


__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
		   'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
		   'wide_resnet50_2', 'wide_resnet101_2']


model_urls = {
	'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
	'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
	'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
	'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
	'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.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, 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, bn_names=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 = batch_norm_multiple(norm_layer, planes, bn_names=bn_names)
		self.relu = nn.ReLU(inplace=True)
		self.conv2 = conv3x3(planes, planes)
		self.bn2 = batch_norm_multiple(norm_layer, planes, bn_names=bn_names)
		self.downsample = downsample
		self.stride = stride

	def forward(self, x):
		identity = x

		out = x[0]
		bn_name = x[1]

		# debug
		# print("bn_name: {}".format(bn_name))

		out = self.conv1(out)
		out = self.bn1([out, bn_name])

		out = self.relu(out)

		out = self.conv2(out)
		out = self.bn2([out, bn_name])

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

		out += identity[0]
		out = self.relu(out)

		return [out, bn_name]


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, bn_names=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 = batch_norm_multiple(norm_layer, width, bn_names=bn_names)
		self.conv2 = conv3x3(width, width, stride, groups, dilation)
		self.bn2 = batch_norm_multiple(norm_layer, width, bn_names=bn_names)
		self.conv3 = conv1x1(width, planes * self.expansion)
		self.bn3 = batch_norm_multiple(norm_layer, planes * self.expansion, bn_names=bn_names)
		self.relu = nn.ReLU(inplace=True)
		self.downsample = downsample
		self.stride = stride

	def forward(self, x):
		identity = x

		out = x[0]
		bn_name = x[1]

		out = self.conv1(out)
		out = self.bn1([out, bn_name])

		out = self.relu(out)

		out = self.conv2(out)
		out = self.bn2([out, bn_name])

		out = self.relu(out)

		out = self.conv3(out)
		out = self.bn3([out, bn_name])

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

		out += identity[0]
		out = self.relu(out)

		return [out, bn_name]


class Downsample_multiple(nn.Module):
	def __init__(self, inplanes, planes, expansion, stride, norm_layer, bn_names=None):
		super(Downsample_multiple, self).__init__()
		self.conv = conv1x1(inplanes, planes * expansion, stride)
		self.bn = batch_norm_multiple(norm_layer, planes * expansion, bn_names=bn_names)

	def forward(self, x):
		out = x[0]
		bn_name = x[1]
		# debug
		# print("adv attack: {}".format(flag_adv))
		# print("out is {}".format(out))

		out = self.conv(out)
		out = self.bn([out, bn_name])

		return [out, bn_name]


class batch_norm_multiple(nn.Module):
	def __init__(self, norm, inplanes, bn_names=None):
		super(batch_norm_multiple, self).__init__()

		# if no bn name input, by default use single bn
		self.bn_names = bn_names
		if self.bn_names is None:
			self.bn_list = norm(inplanes)
			return

		len_bn_names = len(bn_names)
		self.bn_list = nn.ModuleList([norm(inplanes) for _ in range(len_bn_names)])
		self.bn_names_dict = {bn_name: i for i, bn_name in enumerate(bn_names)}
		return

	def forward(self, x):
		out = x[0]
		name_bn = x[1]

		if name_bn is None:
			out = self.bn_list(out)
		else:
			bn_index = self.bn_names_dict[name_bn]
			out = self.bn_list[bn_index](out)

		return out


class proj_head(nn.Module):
	def __init__(self, ch, bn_names=None, twoLayerProj=False):
		super(proj_head, self).__init__()
		self.in_features = ch
		self.twoLayerProj = twoLayerProj

		self.fc1 = nn.Linear(ch, ch)
		self.bn1 = batch_norm_multiple(nn.BatchNorm1d, ch, bn_names)
		self.fc2 = nn.Linear(ch, ch, bias=False)
		self.bn2 = batch_norm_multiple(nn.BatchNorm1d, ch, bn_names)

		if not twoLayerProj:
			self.fc3 = nn.Linear(ch, ch, bias=False)
			self.bn3 = batch_norm_multiple(nn.BatchNorm1d, ch, bn_names)

		self.relu = nn.ReLU(inplace=True)

	def forward(self, x, bn_name):
		# debug
		# print("adv attack: {}".format(flag_adv))

		x = self.fc1(x)
		x = self.bn1([x, bn_name])

		x = self.relu(x)

		x = self.fc2(x)
		x = self.bn2([x, bn_name])

		if not self.twoLayerProj:
			x = self.relu(x)

			x = self.fc3(x)
			x = self.bn3([x, bn_name])

		return x

class predictor(nn.Module):
	def __init__(self, ch, bn_names=None):
		super(predictor, self).__init__()
		self.in_features = ch

		self.fc1 = nn.Linear(ch, ch * 2, bias=False)
		self.bn1 = batch_norm_multiple(nn.BatchNorm1d, ch * 2, bn_names)
		self.relu = nn.ReLU(inplace=True)
		self.fc2 = nn.Linear(ch * 2, ch)

	def forward(self, x, bn_name):

		x = self.fc1(x)
		x = self.bn1([x, bn_name])
		x = self.relu(x)
		x = self.fc2(x)

		return x


class ResNet(nn.Module):
	def __init__(self, block, layers, bn_names, num_classes=1000, zero_init_residual=False,
				 groups=1, width_per_group=64, replace_stride_with_dilation=None,
				 norm_layer=None):
		"""
		:param bn_names: list, the name of bn that would be employed
		"""

		super(ResNet, self).__init__()
		if norm_layer is None:
			norm_layer = nn.BatchNorm2d
		self._norm_layer = norm_layer

		self.normalize = NormalizeByChannelMeanStd(
			mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

		self.inplanes = 64
		self.dilation = 1
		self.bn_names = bn_names

		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 = batch_norm_multiple(norm_layer, self.inplanes, bn_names=self.bn_names)
		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], bn_names=self.bn_names)
		self.layer2 = self._make_layer(block, 128, layers[1], bn_names=self.bn_names,
									   stride=2, dilate=replace_stride_with_dilation[0])
		self.layer3 = self._make_layer(block, 256, layers[2], bn_names=self.bn_names,
									   stride=2, dilate=replace_stride_with_dilation[1])
		self.layer4 = self._make_layer(block, 512, layers[3], bn_names=self.bn_names,
									   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, bn_names=None):
		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 = Downsample_multiple(self.inplanes, planes, block.expansion, stride, norm_layer, bn_names=bn_names)

		layers = []
		layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
							self.base_width, previous_dilation, norm_layer, bn_names=bn_names))
		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, bn_names=bn_names))

		return nn.Sequential(*layers)

	def _forward_impl(self, x, bn_name=None, return_features=False):

		# debug
		# print("bn name: {}".format(bn_name))

		# normalize
		x = self.normalize(x)

		# See note [TorchScript super()]
		x = self.conv1(x)
		x = self.bn1([x, bn_name])

		x = self.relu(x)
		x = self.maxpool(x)

		x = self.layer1([x, bn_name])
		x = self.layer2(x)
		x = self.layer3(x)
		x = self.layer4(x)

		x = self.avgpool(x[0])
		x = torch.flatten(x, 1)

		if return_features:
			return x
		
		if isinstance(self.fc, proj_head):
			x = self.fc(x, bn_name)
		else:
			x = self.fc(x)

		return x

	def forward(self, x, bn_name=None, return_features=False):
		return self._forward_impl(x, bn_name, return_features)

def _resnet(arch, block, layers, pretrained, progress, **kwargs):
	model = ResNet(block, layers, **kwargs)
	if pretrained:
		state_dict = load_state_dict_from_url(model_urls[arch],
											  progress=progress)
		model.load_state_dict(state_dict)
	return model


def resnet18(pretrained=False, progress=True, **kwargs):
	r"""ResNet-18 model from
	`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet
		progress (bool): If True, displays a progress bar of the download to stderr
	"""
	return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
				   **kwargs)


def resnet34(pretrained=False, progress=True, **kwargs):
	r"""ResNet-34 model from
	`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet
		progress (bool): If True, displays a progress bar of the download to stderr
	"""
	return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
				   **kwargs)


def resnet50(pretrained=False, progress=True, **kwargs):
	r"""ResNet-50 model from
	`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet
		progress (bool): If True, displays a progress bar of the download to stderr
	"""
	return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
				   **kwargs)


def resnet101(pretrained=False, progress=True, **kwargs):
	r"""ResNet-101 model from
	`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet
		progress (bool): If True, displays a progress bar of the download to stderr
	"""
	return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
				   **kwargs)


def resnet152(pretrained=False, progress=True, **kwargs):
	r"""ResNet-152 model from
	`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet
		progress (bool): If True, displays a progress bar of the download to stderr
	"""
	return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
				   **kwargs)


def resnext50_32x4d(pretrained=False, progress=True, **kwargs):
	r"""ResNeXt-50 32x4d model from
	`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_

	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet
		progress (bool): If True, displays a progress bar of the download to stderr
	"""
	kwargs['groups'] = 32
	kwargs['width_per_group'] = 4
	return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
				   pretrained, progress, **kwargs)


def resnext101_32x8d(pretrained=False, progress=True, **kwargs):
	r"""ResNeXt-101 32x8d model from
	`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_

	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet
		progress (bool): If True, displays a progress bar of the download to stderr
	"""
	kwargs['groups'] = 32
	kwargs['width_per_group'] = 8
	return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
				   pretrained, progress, **kwargs)


def wide_resnet50_2(pretrained=False, progress=True, **kwargs):
	r"""Wide ResNet-50-2 model from
	`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_

	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.

	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet
		progress (bool): If True, displays a progress bar of the download to stderr
	"""
	kwargs['width_per_group'] = 64 * 2
	return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],
				   pretrained, progress, **kwargs)


def wide_resnet101_2(pretrained=False, progress=True, **kwargs):
	r"""Wide ResNet-101-2 model from
	`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_

	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.

	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet
		progress (bool): If True, displays a progress bar of the download to stderr
	"""
	kwargs['width_per_group'] = 64 * 2
	return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
				   pretrained, progress, **kwargs)

class resnet18_momentum(nn.Module):
	def __init__(self, pretrained=False, m=0.99, simsiam=False, **kwargs):
		super(resnet18_momentum, self).__init__()
		self.m = m
		self.encoder_q = resnet18(pretrained=pretrained, **kwargs)
		self.encoder_k = resnet18(pretrained=pretrained, **kwargs)
		self.out_dim = 512
		self.simsiam = simsiam

	@torch.no_grad()
	def _init_encoder_k(self):
		for param in self.encoder_k.parameters():
			param.requires_grad = False

		for param_q, param_k in zip(self.encoder_q.parameters(), self.encoder_k.parameters()):
				param_k.data.copy_(param_q.data)  # initialize

	@torch.no_grad()
	def _momentum_update_encoder_k(self):
		"""
		Momentum update of the key encoder
		"""
		if not self.simsiam:
			for param_q, param_k in zip(self.encoder_q.parameters(), self.encoder_k.parameters()):
					param_k.data = param_k.data * self.m + param_q.data * (1. - self.m)
		else:
			for param_q, param_k in zip(self.encoder_q.parameters(), self.encoder_k.parameters()):
					param_k.data.copy_(param_q.data)

	def forward(self, x, bn_name=None, swap=False):
		"""
		Args:
			x: input tensor
			bn_name: name of the batchnorm layer
		"""
		if swap:
			bsz = x.size()[0]
			out = torch.zeros(bsz, self.out_dim)
			out[::2] = self.encoder_q(x[::2], bn_name=bn_name)
			out[1::2] = self.encoder_k(x[1::2], bn_name=bn_name)
			return out
		else:
			return self.encoder_k(x, bn_name)

