# Copyright 2022 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.

"""Implements customized ResNets that return embeddings and logits."""

from typing import Any, Callable, List, Optional, Type, Union

import torch
from torch import nn


__all__ = ["ResNet", "resnet18", "resnet34", "resnet50", "resnet101"]


def conv3x3(in_planes: int,
            out_planes: int,
            stride: int = 1,
            groups: int = 1,
            dilation: int = 1) -> nn.Conv2d:
  """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: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
  """1x1 convolution."""
  return nn.Conv2d(
      in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
  """A basic residual block with optional learned downsampling."""
  expansion: int = 1

  def __init__(
      self,
      inplanes: int,
      planes: int,
      stride: int = 1,
      downsample: Optional[nn.Module] = None,
      groups: int = 1,
      base_width: int = 64,
      dilation: int = 1,
      norm_layer: Optional[Callable[..., nn.Module]] = None,
  ) -> None:
    super().__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: torch.Tensor) -> torch.Tensor:
    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):
  """A bottleneck residual block."""
  # 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: int = 4

  def __init__(
      self,
      inplanes: int,
      planes: int,
      stride: int = 1,
      downsample: Optional[nn.Module] = None,
      groups: int = 1,
      base_width: int = 64,
      dilation: int = 1,
      norm_layer: Optional[Callable[..., nn.Module]] = None,
  ) -> None:
    super().__init__()
    if norm_layer is None:
      norm_layer = nn.BatchNorm2d
    width = int(planes * (base_width / 64.0)) * 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: torch.Tensor) -> torch.Tensor:
    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):
  """A residual network architecture."""

  def __init__(self,
               block: Type[Union[BasicBlock, Bottleneck]],
               layers: List[int],
               num_classes: int = 1000,
               zero_init_residual: bool = False,
               groups: int = 1,
               width_per_group: int = 64,
               replace_stride_with_dilation: Optional[List[bool]] = None,
               norm_layer: Optional[Callable[..., nn.Module]] = None,
               name: Optional[str] = None) -> None:
    super().__init__()
    if norm_layer is None:
      norm_layer = nn.BatchNorm2d
    self._norm_layer = norm_layer

    self.inplanes = 64
    self.dilation = 1
    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 "
          f"or a 3-element tuple, got {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, 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.features_dim = 512

    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) and m.bn3.weight is not None:
          nn.init.constant_(m.bn3.weight, 0)  # type: ignore[arg-type]
        elif isinstance(m, BasicBlock) and m.bn2.weight is not None:
          nn.init.constant_(m.bn2.weight, 0)  # type: ignore[arg-type]

  def _make_layer(
      self,
      block: Type[Union[BasicBlock, Bottleneck]],
      planes: int,
      blocks: int,
      stride: int = 1,
      dilate: bool = False,
  ) -> nn.Sequential:
    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: torch.Tensor) -> torch.Tensor:
    # See note [TorchScript super()]
    x = x.permute((0, 3, 1, 2))
    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)

    return x

  def forward(self, x: torch.Tensor) -> torch.Tensor:
    return self._forward_impl(x)


def _resnet(
    block: Type[Union[BasicBlock, Bottleneck]],
    layers: List[int],
    **kwargs: Any,
) -> ResNet:
  model = ResNet(block, layers, **kwargs)
  return model


def resnet18(**kwargs: Any) -> ResNet:
  return _resnet(BasicBlock, [2, 2, 2, 2], **kwargs)


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


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


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