# 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.

"""From https://github.com/deepmind/deepmind-research/blob/master/adversarial_robustness/pytorch/model_zoo.py"""
from typing import Tuple, Union

import torch
import torch.nn as nn
import torch.nn.functional as F


CIFAR10_MEAN = (0.4914, 0.4822, 0.4465)
CIFAR10_STD = (0.2471, 0.2435, 0.2616)
CIFAR100_MEAN = (0.5071, 0.4865, 0.4409)
CIFAR100_STD = (0.2673, 0.2564, 0.2762)


class _Swish(torch.autograd.Function):
  """Custom implementation of swish."""

  @staticmethod
  def forward(ctx, i):
    result = i * torch.sigmoid(i)
    ctx.save_for_backward(i)
    return result

  @staticmethod
  def backward(ctx, grad_output):
    i = ctx.saved_variables[0]
    sigmoid_i = torch.sigmoid(i)
    return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i)))


class Swish(nn.Module):
  """Module using custom implementation."""

  def forward(self, input_tensor):
    return _Swish.apply(input_tensor)


class _Block(nn.Module):
  """WideResNet Block."""

  def __init__(self, in_planes, out_planes, stride, activation_fn=nn.ReLU):
    super().__init__()
    self.batchnorm_0 = nn.BatchNorm2d(in_planes)
    self.relu_0 = activation_fn()
    # We manually pad to obtain the same effect as `SAME` (necessary when
    # `stride` is different than 1).
    self.conv_0 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                            padding=0, bias=False)
    self.batchnorm_1 = nn.BatchNorm2d(out_planes)
    self.relu_1 = activation_fn()
    self.conv_1 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1,
                            padding=1, bias=False)
    self.has_shortcut = in_planes != out_planes
    if self.has_shortcut:
      self.shortcut = nn.Conv2d(in_planes, out_planes, kernel_size=1,
                                stride=stride, padding=0, bias=False)
    else:
      self.shortcut = None
    self._stride = stride

  def forward(self, x):
    if self.has_shortcut:
      x = self.relu_0(self.batchnorm_0(x))
    else:
      out = self.relu_0(self.batchnorm_0(x))
    v = x if self.has_shortcut else out
    if self._stride == 1:
      v = F.pad(v, (1, 1, 1, 1))
    elif self._stride == 2:
      v = F.pad(v, (0, 1, 0, 1))
    else:
      raise ValueError('Unsupported `stride`.')
    out = self.conv_0(v)
    out = self.relu_1(self.batchnorm_1(out))
    out = self.conv_1(out)
    out = torch.add(self.shortcut(x) if self.has_shortcut else x, out)
    return out


class _BlockGroup(nn.Module):
  """WideResNet block group."""

  def __init__(self, num_blocks, in_planes, out_planes, stride,
      activation_fn=nn.ReLU):
    super().__init__()
    block = []
    for i in range(num_blocks):
      block.append(
          _Block(i == 0 and in_planes or out_planes,
                 out_planes,
                 i == 0 and stride or 1,
                 activation_fn=activation_fn))
    self.block = nn.Sequential(*block)

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


class WideResNet(nn.Module):
  """WideResNet."""

  def __init__(self,
      num_classes: int = 10,
      depth: int = 28,
      width: int = 10,
      activation_fn: nn.Module = nn.ReLU,
      mean: Union[Tuple[float, ...], float] = CIFAR10_MEAN,
      std: Union[Tuple[float, ...], float] = CIFAR10_STD,
      padding: int = 0,
      num_input_channels: int = 3):
    super().__init__()
    self.mean = torch.tensor(mean).view(num_input_channels, 1, 1)
    self.std = torch.tensor(std).view(num_input_channels, 1, 1)
    self.mean_cuda = None
    self.std_cuda = None
    self.padding = padding
    num_channels = [16, 16 * width, 32 * width, 64 * width]
    assert (depth - 4) % 6 == 0
    num_blocks = (depth - 4) // 6
    self.init_conv = nn.Conv2d(num_input_channels, num_channels[0],
                               kernel_size=3, stride=1, padding=1, bias=False)
    self.layer = nn.Sequential(
        _BlockGroup(num_blocks, num_channels[0], num_channels[1], 1,
                    activation_fn=activation_fn),
        _BlockGroup(num_blocks, num_channels[1], num_channels[2], 2,
                    activation_fn=activation_fn),
        _BlockGroup(num_blocks, num_channels[2], num_channels[3], 2,
                    activation_fn=activation_fn))
    self.batchnorm = nn.BatchNorm2d(num_channels[3])
    self.relu = activation_fn()
    self.logits = nn.Linear(num_channels[3], num_classes)
    self.num_channels = num_channels[3]

  def forward(self, x, features_only=False, features_and_logits=False):
    if self.padding > 0:
      x = F.pad(x, (self.padding,) * 4)
    if x.is_cuda:
      if self.mean_cuda is None:
        self.mean_cuda = self.mean.cuda()
        self.std_cuda = self.std.cuda()
      out = (x - self.mean_cuda) / self.std_cuda
    else:
      out = (x - self.mean) / self.std
    out = self.init_conv(out)
    out = self.layer(out)
    out = self.relu(self.batchnorm(out))
    out = F.avg_pool2d(out, 8)
    features = out.view(-1, self.num_channels)
    if features_only:
      return features
    logits = self.logits(features)
    if features_and_logits:
      return features, logits
    return logits


class _PreActBlock(nn.Module):
  """Pre-activation ResNet Block."""

  def __init__(self, in_planes, out_planes, stride, activation_fn=nn.ReLU):
    super().__init__()
    self._stride = stride
    self.batchnorm_0 = nn.BatchNorm2d(in_planes)
    self.relu_0 = activation_fn()
    # We manually pad to obtain the same effect as `SAME` (necessary when
    # `stride` is different than 1).
    self.conv_2d_1 = nn.Conv2d(in_planes, out_planes, kernel_size=3,
                               stride=stride, padding=0, bias=False)
    self.batchnorm_1 = nn.BatchNorm2d(out_planes)
    self.relu_1 = activation_fn()
    self.conv_2d_2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1,
                               padding=1, bias=False)
    self.has_shortcut = stride != 1 or in_planes != out_planes
    if self.has_shortcut:
      self.shortcut = nn.Conv2d(in_planes, out_planes, kernel_size=3,
                                stride=stride, padding=0, bias=False)

  def _pad(self, x):
    if self._stride == 1:
      x = F.pad(x, (1, 1, 1, 1))
    elif self._stride == 2:
      x = F.pad(x, (0, 1, 0, 1))
    else:
      raise ValueError('Unsupported `stride`.')
    return x

  def forward(self, x):
    out = self.relu_0(self.batchnorm_0(x))
    shortcut = self.shortcut(self._pad(x)) if self.has_shortcut else x
    out = self.conv_2d_1(self._pad(out))
    out = self.conv_2d_2(self.relu_1(self.batchnorm_1(out)))
    return out + shortcut


class PreActResNet(nn.Module):
  """Pre-activation ResNet."""

  def __init__(self,
      num_classes: int = 10,
      depth: int = 18,
      width: int = 0,  # Used to make the constructor consistent.
      activation_fn: nn.Module = nn.ReLU,
      mean: Union[Tuple[float, ...], float] = CIFAR10_MEAN,
      std: Union[Tuple[float, ...], float] = CIFAR10_STD,
      padding: int = 0,
      num_input_channels: int = 3):
    super().__init__()
    if width != 0:
      raise ValueError('Unsupported `width`.')
    self.mean = torch.tensor(mean).view(num_input_channels, 1, 1)
    self.std = torch.tensor(std).view(num_input_channels, 1, 1)
    self.mean_cuda = None
    self.std_cuda = None
    self.padding = padding
    self.conv_2d = nn.Conv2d(num_input_channels, 64, kernel_size=3, stride=1,
                             padding=1, bias=False)
    if depth == 18:
      num_blocks = (2, 2, 2, 2)
    elif depth == 34:
      num_blocks = (3, 4, 6, 3)
    else:
      raise ValueError('Unsupported `depth`.')
    self.layer_0 = self._make_layer(64, 64, num_blocks[0], 1, activation_fn)
    self.layer_1 = self._make_layer(64, 128, num_blocks[1], 2, activation_fn)
    self.layer_2 = self._make_layer(128, 256, num_blocks[2], 2, activation_fn)
    self.layer_3 = self._make_layer(256, 512, num_blocks[3], 2, activation_fn)
    self.batchnorm = nn.BatchNorm2d(512)
    self.relu = activation_fn()
    self.logits = nn.Linear(512, num_classes)

  def _make_layer(self, in_planes, out_planes, num_blocks, stride,
      activation_fn):
    layers = []
    for i, stride in enumerate([stride] + [1] * (num_blocks - 1)):
      layers.append(
          _PreActBlock(i == 0 and in_planes or out_planes,
                       out_planes,
                       stride,
                       activation_fn))
    return nn.Sequential(*layers)

  def forward(self, x, features_only=False, features_and_logits=False):
    if self.padding > 0:
      x = F.pad(x, (self.padding,) * 4)
    if x.is_cuda:
      if self.mean_cuda is None:
        self.mean_cuda = self.mean.cuda()
        self.std_cuda = self.std.cuda()
      out = (x - self.mean_cuda) / self.std_cuda
    else:
      out = (x - self.mean) / self.std
    out = self.conv_2d(out)
    out = self.layer_0(out)
    out = self.layer_1(out)
    out = self.layer_2(out)
    out = self.layer_3(out)
    out = self.relu(self.batchnorm(out))
    out = F.avg_pool2d(out, 4)
    features = out.view(out.size(0), -1)
    if features_only:
      return features
    logits = self.logits(features)
    if features_and_logits:
      return features, logits
    return logits


def wideresnet_28_10(num_classes=10):
  return WideResNet(num_classes, 28, 10, activation_fn=Swish, mean=CIFAR10_MEAN,
                    std=CIFAR10_STD)

def preactresnet_18(num_classes=10):
  return PreActResNet(num_classes=num_classes, depth=18, activation_fn=Swish,
                      mean=CIFAR10_MEAN, std=CIFAR10_STD)