# Copyright (c) OpenMMLab. All rights reserved.
import copy

import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule, build_conv_layer, build_norm_layer
from mmcv.cnn.bricks import ContextBlock
from mmcv.utils.parrots_wrapper import _BatchNorm

from ..builder import BACKBONES
from .base_backbone import BaseBackbone


class ViPNAS_Bottleneck(nn.Module):
    """Bottleneck block for ViPNAS_ResNet.

    Args:
        in_channels (int): Input channels of this block.
        out_channels (int): Output channels of this block.
        expansion (int): The ratio of ``out_channels/mid_channels`` where
            ``mid_channels`` is the input/output channels of conv2. Default: 4.
        stride (int): stride of the block. Default: 1
        dilation (int): dilation of convolution. Default: 1
        downsample (nn.Module): downsample operation on identity branch.
            Default: None.
        style (str): ``"pytorch"`` or ``"caffe"``. If set to "pytorch", the
            stride-two layer is the 3x3 conv layer, otherwise the stride-two
            layer is the first 1x1 conv layer. Default: "pytorch".
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed.
        conv_cfg (dict): dictionary to construct and config conv layer.
            Default: None
        norm_cfg (dict): dictionary to construct and config norm layer.
            Default: dict(type='BN')
        kernel_size (int): kernel size of conv2 searched in ViPANS.
        groups (int): group number of conv2 searched in ViPNAS.
        attention (bool): whether to use attention module in the end of
            the block.
    """

    def __init__(self,
                 in_channels,
                 out_channels,
                 expansion=4,
                 stride=1,
                 dilation=1,
                 downsample=None,
                 style='pytorch',
                 with_cp=False,
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 kernel_size=3,
                 groups=1,
                 attention=False):
        # Protect mutable default arguments
        norm_cfg = copy.deepcopy(norm_cfg)
        super().__init__()
        assert style in ['pytorch', 'caffe']

        self.in_channels = in_channels
        self.out_channels = out_channels
        self.expansion = expansion
        assert out_channels % expansion == 0
        self.mid_channels = out_channels // expansion
        self.stride = stride
        self.dilation = dilation
        self.style = style
        self.with_cp = with_cp
        self.conv_cfg = conv_cfg
        self.norm_cfg = norm_cfg

        if self.style == 'pytorch':
            self.conv1_stride = 1
            self.conv2_stride = stride
        else:
            self.conv1_stride = stride
            self.conv2_stride = 1

        self.norm1_name, norm1 = build_norm_layer(
            norm_cfg, self.mid_channels, postfix=1)
        self.norm2_name, norm2 = build_norm_layer(
            norm_cfg, self.mid_channels, postfix=2)
        self.norm3_name, norm3 = build_norm_layer(
            norm_cfg, out_channels, postfix=3)

        self.conv1 = build_conv_layer(
            conv_cfg,
            in_channels,
            self.mid_channels,
            kernel_size=1,
            stride=self.conv1_stride,
            bias=False)
        self.add_module(self.norm1_name, norm1)
        self.conv2 = build_conv_layer(
            conv_cfg,
            self.mid_channels,
            self.mid_channels,
            kernel_size=kernel_size,
            stride=self.conv2_stride,
            padding=kernel_size // 2,
            groups=groups,
            dilation=dilation,
            bias=False)

        self.add_module(self.norm2_name, norm2)
        self.conv3 = build_conv_layer(
            conv_cfg,
            self.mid_channels,
            out_channels,
            kernel_size=1,
            bias=False)
        self.add_module(self.norm3_name, norm3)

        if attention:
            self.attention = ContextBlock(out_channels,
                                          max(1.0 / 16, 16.0 / out_channels))
        else:
            self.attention = None

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

    @property
    def norm1(self):
        """nn.Module: the normalization layer named "norm1" """
        return getattr(self, self.norm1_name)

    @property
    def norm2(self):
        """nn.Module: the normalization layer named "norm2" """
        return getattr(self, self.norm2_name)

    @property
    def norm3(self):
        """nn.Module: the normalization layer named "norm3" """
        return getattr(self, self.norm3_name)

    def forward(self, x):
        """Forward function."""

        def _inner_forward(x):
            identity = x

            out = self.conv1(x)
            out = self.norm1(out)
            out = self.relu(out)

            out = self.conv2(out)
            out = self.norm2(out)
            out = self.relu(out)

            out = self.conv3(out)
            out = self.norm3(out)

            if self.attention is not None:
                out = self.attention(out)

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

            out += identity

            return out

        if self.with_cp and x.requires_grad:
            out = cp.checkpoint(_inner_forward, x)
        else:
            out = _inner_forward(x)

        out = self.relu(out)

        return out


def get_expansion(block, expansion=None):
    """Get the expansion of a residual block.

    The block expansion will be obtained by the following order:

    1. If ``expansion`` is given, just return it.
    2. If ``block`` has the attribute ``expansion``, then return
       ``block.expansion``.
    3. Return the default value according the the block type:
       4 for ``ViPNAS_Bottleneck``.

    Args:
        block (class): The block class.
        expansion (int | None): The given expansion ratio.

    Returns:
        int: The expansion of the block.
    """
    if isinstance(expansion, int):
        assert expansion > 0
    elif expansion is None:
        if hasattr(block, 'expansion'):
            expansion = block.expansion
        elif issubclass(block, ViPNAS_Bottleneck):
            expansion = 1
        else:
            raise TypeError(f'expansion is not specified for {block.__name__}')
    else:
        raise TypeError('expansion must be an integer or None')

    return expansion


class ViPNAS_ResLayer(nn.Sequential):
    """ViPNAS_ResLayer to build ResNet style backbone.

    Args:
        block (nn.Module): Residual block used to build ViPNAS ResLayer.
        num_blocks (int): Number of blocks.
        in_channels (int): Input channels of this block.
        out_channels (int): Output channels of this block.
        expansion (int, optional): The expansion for BasicBlock/Bottleneck.
            If not specified, it will firstly be obtained via
            ``block.expansion``. If the block has no attribute "expansion",
            the following default values will be used: 1 for BasicBlock and
            4 for Bottleneck. Default: None.
        stride (int): stride of the first block. Default: 1.
        avg_down (bool): Use AvgPool instead of stride conv when
            downsampling in the bottleneck. Default: False
        conv_cfg (dict): dictionary to construct and config conv layer.
            Default: None
        norm_cfg (dict): dictionary to construct and config norm layer.
            Default: dict(type='BN')
        downsample_first (bool): Downsample at the first block or last block.
            False for Hourglass, True for ResNet. Default: True
        kernel_size (int): Kernel Size of the corresponding convolution layer
            searched in the block.
        groups (int): Group number of the corresponding convolution layer
            searched in the block.
        attention (bool): Whether to use attention module in the end of the
            block.
    """

    def __init__(self,
                 block,
                 num_blocks,
                 in_channels,
                 out_channels,
                 expansion=None,
                 stride=1,
                 avg_down=False,
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 downsample_first=True,
                 kernel_size=3,
                 groups=1,
                 attention=False,
                 **kwargs):
        # Protect mutable default arguments
        norm_cfg = copy.deepcopy(norm_cfg)
        self.block = block
        self.expansion = get_expansion(block, expansion)

        downsample = None
        if stride != 1 or in_channels != out_channels:
            downsample = []
            conv_stride = stride
            if avg_down and stride != 1:
                conv_stride = 1
                downsample.append(
                    nn.AvgPool2d(
                        kernel_size=stride,
                        stride=stride,
                        ceil_mode=True,
                        count_include_pad=False))
            downsample.extend([
                build_conv_layer(
                    conv_cfg,
                    in_channels,
                    out_channels,
                    kernel_size=1,
                    stride=conv_stride,
                    bias=False),
                build_norm_layer(norm_cfg, out_channels)[1]
            ])
            downsample = nn.Sequential(*downsample)

        layers = []
        if downsample_first:
            layers.append(
                block(
                    in_channels=in_channels,
                    out_channels=out_channels,
                    expansion=self.expansion,
                    stride=stride,
                    downsample=downsample,
                    conv_cfg=conv_cfg,
                    norm_cfg=norm_cfg,
                    kernel_size=kernel_size,
                    groups=groups,
                    attention=attention,
                    **kwargs))
            in_channels = out_channels
            for _ in range(1, num_blocks):
                layers.append(
                    block(
                        in_channels=in_channels,
                        out_channels=out_channels,
                        expansion=self.expansion,
                        stride=1,
                        conv_cfg=conv_cfg,
                        norm_cfg=norm_cfg,
                        kernel_size=kernel_size,
                        groups=groups,
                        attention=attention,
                        **kwargs))
        else:  # downsample_first=False is for HourglassModule
            for i in range(0, num_blocks - 1):
                layers.append(
                    block(
                        in_channels=in_channels,
                        out_channels=in_channels,
                        expansion=self.expansion,
                        stride=1,
                        conv_cfg=conv_cfg,
                        norm_cfg=norm_cfg,
                        kernel_size=kernel_size,
                        groups=groups,
                        attention=attention,
                        **kwargs))
            layers.append(
                block(
                    in_channels=in_channels,
                    out_channels=out_channels,
                    expansion=self.expansion,
                    stride=stride,
                    downsample=downsample,
                    conv_cfg=conv_cfg,
                    norm_cfg=norm_cfg,
                    kernel_size=kernel_size,
                    groups=groups,
                    attention=attention,
                    **kwargs))

        super().__init__(*layers)


@BACKBONES.register_module()
class ViPNAS_ResNet(BaseBackbone):
    """ResNet backbone.

    ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search.
    More details can be found in the `paper
    <https://arxiv.org/abs/2105.10154>`__ .

    Args:
        depth (int): Network depth, from {18, 34, 50, 101, 152}.
        in_channels (int): Number of input image channels. Default: 3.
        num_stages (int): Stages of the network. Default: 4.
        strides (Sequence[int]): Strides of the first block of each stage.
            Default: ``(1, 2, 2, 2)``.
        dilations (Sequence[int]): Dilation of each stage.
            Default: ``(1, 1, 1, 1)``.
        out_indices (Sequence[int]): Output from which stages. If only one
            stage is specified, a single tensor (feature map) is returned,
            otherwise multiple stages are specified, a tuple of tensors will
            be returned. Default: ``(3, )``.
        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
            layer is the 3x3 conv layer, otherwise the stride-two layer is
            the first 1x1 conv layer.
        deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv.
            Default: False.
        avg_down (bool): Use AvgPool instead of stride conv when
            downsampling in the bottleneck. Default: False.
        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
            -1 means not freezing any parameters. Default: -1.
        conv_cfg (dict | None): The config dict for conv layers. Default: None.
        norm_cfg (dict): The config dict for norm layers.
        norm_eval (bool): Whether to set norm layers to eval mode, namely,
            freeze running stats (mean and var). Note: Effect on Batch Norm
            and its variants only. Default: False.
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed. Default: False.
        zero_init_residual (bool): Whether to use zero init for last norm layer
            in resblocks to let them behave as identity. Default: True.
        wid (list(int)): searched width config for each stage.
        expan (list(int)): searched expansion ratio config for each stage.
        dep (list(int)): searched depth config for each stage.
        ks (list(int)): searched kernel size config for each stage.
        group (list(int)): searched group number config for each stage.
        att (list(int)): searched attention config for each stage.
    """

    arch_settings = {
        50: ViPNAS_Bottleneck,
    }

    def __init__(self,
                 depth,
                 in_channels=3,
                 num_stages=4,
                 strides=(1, 2, 2, 2),
                 dilations=(1, 1, 1, 1),
                 out_indices=(3, ),
                 style='pytorch',
                 deep_stem=False,
                 avg_down=False,
                 frozen_stages=-1,
                 conv_cfg=None,
                 norm_cfg=dict(type='BN', requires_grad=True),
                 norm_eval=False,
                 with_cp=False,
                 zero_init_residual=True,
                 wid=[48, 80, 160, 304, 608],
                 expan=[None, 1, 1, 1, 1],
                 dep=[None, 4, 6, 7, 3],
                 ks=[7, 3, 5, 5, 5],
                 group=[None, 16, 16, 16, 16],
                 att=[None, True, False, True, True]):
        # Protect mutable default arguments
        norm_cfg = copy.deepcopy(norm_cfg)
        super().__init__()
        if depth not in self.arch_settings:
            raise KeyError(f'invalid depth {depth} for resnet')
        self.depth = depth
        self.stem_channels = dep[0]
        self.num_stages = num_stages
        assert 1 <= num_stages <= 4
        self.strides = strides
        self.dilations = dilations
        assert len(strides) == len(dilations) == num_stages
        self.out_indices = out_indices
        assert max(out_indices) < num_stages
        self.style = style
        self.deep_stem = deep_stem
        self.avg_down = avg_down
        self.frozen_stages = frozen_stages
        self.conv_cfg = conv_cfg
        self.norm_cfg = norm_cfg
        self.with_cp = with_cp
        self.norm_eval = norm_eval
        self.zero_init_residual = zero_init_residual
        self.block = self.arch_settings[depth]
        self.stage_blocks = dep[1:1 + num_stages]

        self._make_stem_layer(in_channels, wid[0], ks[0])

        self.res_layers = []
        _in_channels = wid[0]
        for i, num_blocks in enumerate(self.stage_blocks):
            expansion = get_expansion(self.block, expan[i + 1])
            _out_channels = wid[i + 1] * expansion
            stride = strides[i]
            dilation = dilations[i]
            res_layer = self.make_res_layer(
                block=self.block,
                num_blocks=num_blocks,
                in_channels=_in_channels,
                out_channels=_out_channels,
                expansion=expansion,
                stride=stride,
                dilation=dilation,
                style=self.style,
                avg_down=self.avg_down,
                with_cp=with_cp,
                conv_cfg=conv_cfg,
                norm_cfg=norm_cfg,
                kernel_size=ks[i + 1],
                groups=group[i + 1],
                attention=att[i + 1])
            _in_channels = _out_channels
            layer_name = f'layer{i + 1}'
            self.add_module(layer_name, res_layer)
            self.res_layers.append(layer_name)

        self._freeze_stages()

        self.feat_dim = res_layer[-1].out_channels

    def make_res_layer(self, **kwargs):
        """Make a ViPNAS ResLayer."""
        return ViPNAS_ResLayer(**kwargs)

    @property
    def norm1(self):
        """nn.Module: the normalization layer named "norm1" """
        return getattr(self, self.norm1_name)

    def _make_stem_layer(self, in_channels, stem_channels, kernel_size):
        """Make stem layer."""
        if self.deep_stem:
            self.stem = nn.Sequential(
                ConvModule(
                    in_channels,
                    stem_channels // 2,
                    kernel_size=3,
                    stride=2,
                    padding=1,
                    conv_cfg=self.conv_cfg,
                    norm_cfg=self.norm_cfg,
                    inplace=True),
                ConvModule(
                    stem_channels // 2,
                    stem_channels // 2,
                    kernel_size=3,
                    stride=1,
                    padding=1,
                    conv_cfg=self.conv_cfg,
                    norm_cfg=self.norm_cfg,
                    inplace=True),
                ConvModule(
                    stem_channels // 2,
                    stem_channels,
                    kernel_size=3,
                    stride=1,
                    padding=1,
                    conv_cfg=self.conv_cfg,
                    norm_cfg=self.norm_cfg,
                    inplace=True))
        else:
            self.conv1 = build_conv_layer(
                self.conv_cfg,
                in_channels,
                stem_channels,
                kernel_size=kernel_size,
                stride=2,
                padding=kernel_size // 2,
                bias=False)
            self.norm1_name, norm1 = build_norm_layer(
                self.norm_cfg, stem_channels, postfix=1)
            self.add_module(self.norm1_name, norm1)
            self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

    def _freeze_stages(self):
        """Freeze parameters."""
        if self.frozen_stages >= 0:
            if self.deep_stem:
                self.stem.eval()
                for param in self.stem.parameters():
                    param.requires_grad = False
            else:
                self.norm1.eval()
                for m in [self.conv1, self.norm1]:
                    for param in m.parameters():
                        param.requires_grad = False

        for i in range(1, self.frozen_stages + 1):
            m = getattr(self, f'layer{i}')
            m.eval()
            for param in m.parameters():
                param.requires_grad = False

    def init_weights(self, pretrained=None):

        super().init_weights(pretrained)
        if pretrained is None:
            for m in self.modules():
                if isinstance(m, nn.Conv2d):
                    nn.init.normal_(m.weight, std=0.001)
                    for name, _ in m.named_parameters():
                        if name in ['bias']:
                            nn.init.constant_(m.bias, 0)
                elif isinstance(m, nn.BatchNorm2d):
                    nn.init.constant_(m.weight, 1)
                    nn.init.constant_(m.bias, 0)
                elif isinstance(m, nn.ConvTranspose2d):
                    nn.init.normal_(m.weight, std=0.001)
                    for name, _ in m.named_parameters():
                        if name in ['bias']:
                            nn.init.constant_(m.bias, 0)
                elif isinstance(m, nn.ConvTranspose2d):
                    nn.init.normal_(m.weight, std=0.001)
                    if self.deconv_with_bias:
                        nn.init.constant_(m.bias, 0)

    def forward(self, x):
        """Forward function."""
        if self.deep_stem:
            x = self.stem(x)
        else:
            x = self.conv1(x)
            x = self.norm1(x)
            x = self.relu(x)
        x = self.maxpool(x)
        outs = []
        for i, layer_name in enumerate(self.res_layers):
            res_layer = getattr(self, layer_name)
            x = res_layer(x)
            if i in self.out_indices:
                outs.append(x)
        if len(outs) == 1:
            return outs[0]
        return tuple(outs)

    def train(self, mode=True):
        """Convert the model into training mode."""
        super().train(mode)
        self._freeze_stages()
        if mode and self.norm_eval:
            for m in self.modules():
                # trick: eval have effect on BatchNorm only
                if isinstance(m, _BatchNorm):
                    m.eval()
