# Copyright 2020 Deepmind Technologies Limited.
#
# 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
#
#     http://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.

"""WideResNet implementation in PyTorch. From:
https://github.com/deepmind/deepmind-research/blob/master/adversarial_robustness/pytorch/model_zoo.py
"""

from typing import Tuple, Type, 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: Type[nn.Module] = 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: Type[nn.Module] = 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 DMWideResNet(nn.Module):
    """WideResNet."""
    def __init__(self,
                 num_classes: int = 10,
                 depth: int = 28,
                 width: int = 10,
                 activation_fn: Type[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.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):
        if self.padding > 0:
            x = F.pad(x, (self.padding, ) * 4)
        out = (x - self.mean.to(x.device)) / self.std.to(x.device)
        out = self.init_conv(out)
        out = self.layer(out)
        out = self.relu(self.batchnorm(out))
        out = F.avg_pool2d(out, 8)
        out = out.view(-1, self.num_channels)
        return self.logits(out)


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 DMPreActResNet(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,
               use_cuda: bool = True):
    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):
    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)
    out = out.view(out.size(0), -1)
    return self.logits(out)

