# Copyright (c) 2021 Shuai Wang (wsstriving@gmail.com)
#               2022 Zhengyang Chen (chenzhengyang117@gmail.com)
#               2023 Bing Han (hanbing97@sjtu.edu.cn)
#
# 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.

"""ResNet in PyTorch.

Some modifications from the original architecture:
1. Smaller kernel size for the input layer
2. Smaller number of Channels
3. No max_pooling involved

Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
    Deep Residual Learning for Image Recognition. arXiv:1512.03385
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
import dguard.models.resnet.pooling_layers as pooling_layers


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(
            in_planes,
            planes,
            kernel_size=3,
            stride=stride,
            padding=1,
            bias=False,
        )
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(
            planes, planes, kernel_size=3, stride=1, padding=1, bias=False
        )
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(
                    in_planes,
                    self.expansion * planes,
                    kernel_size=1,
                    stride=stride,
                    bias=False,
                ),
                nn.BatchNorm2d(self.expansion * planes),
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_planes, planes, stride=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(
            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False
        )
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(
            planes, self.expansion * planes, kernel_size=1, bias=False
        )
        self.bn3 = nn.BatchNorm2d(self.expansion * planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(
                    in_planes,
                    self.expansion * planes,
                    kernel_size=1,
                    stride=stride,
                    bias=False,
                ),
                nn.BatchNorm2d(self.expansion * planes),
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class ResNet(nn.Module):
    def __init__(
        self,
        num_blocks,
        block_type,
        m_channels=32,
        feat_dim=40,
        embed_dim=128,
        pooling_func="TSTP",
        two_emb_layer=True,
    ):
        super(ResNet, self).__init__()
        self.in_planes = m_channels
        self.feat_dim = feat_dim
        self.embed_dim = embed_dim
        self.stats_dim = int(feat_dim / 8) * m_channels * 8
        self.two_emb_layer = two_emb_layer
        if block_type == "BasicBlock":
            block = BasicBlock
        elif block_type == "Bottleneck":
            block = Bottleneck
        else:
            raise ValueError(f"block_type {block_type} not supported !!!")

        self.conv1 = nn.Conv2d(
            1, m_channels, kernel_size=3, stride=1, padding=1, bias=False
        )
        self.bn1 = nn.BatchNorm2d(m_channels)
        self.layer1 = self._make_layer(
            block, m_channels, num_blocks[0], stride=1
        )
        self.layer2 = self._make_layer(
            block, m_channels * 2, num_blocks[1], stride=2
        )
        self.layer3 = self._make_layer(
            block, m_channels * 4, num_blocks[2], stride=2
        )
        self.layer4 = self._make_layer(
            block, m_channels * 8, num_blocks[3], stride=2
        )

        self.pool = getattr(pooling_layers, pooling_func)(
            in_dim=self.stats_dim * block.expansion
        )
        self.pool_out_dim = self.pool.get_out_dim()
        self.seg_1 = nn.Linear(self.pool_out_dim, embed_dim)
        if self.two_emb_layer:
            self.seg_bn_1 = nn.BatchNorm1d(embed_dim, affine=False)
            self.seg_2 = nn.Linear(embed_dim, embed_dim)
        else:
            self.seg_bn_1 = nn.Identity()
            self.seg_2 = nn.Identity()

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        x = x.permute(0, 2, 1)  # (B,T,F) => (B,F,T)

        x = x.unsqueeze_(1)
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)

        stats = self.pool(out)

        embed_a = self.seg_1(stats)
        if self.two_emb_layer:
            out = F.relu(embed_a)
            out = self.seg_bn_1(out)
            embed_b = self.seg_2(out)
            return embed_a, embed_b
        else:
            return torch.tensor(0.0), embed_a


def ResNet18(feat_dim, embed_dim, pooling_func="TSTP", two_emb_layer=True):
    return ResNet(
        BasicBlock,
        [2, 2, 2, 2],
        feat_dim=feat_dim,
        embed_dim=embed_dim,
        pooling_func=pooling_func,
        two_emb_layer=two_emb_layer,
    )


def ResNet34(feat_dim, embed_dim, pooling_func="TSTP", two_emb_layer=True):
    return ResNet(
        BasicBlock,
        [3, 4, 6, 3],
        feat_dim=feat_dim,
        embed_dim=embed_dim,
        pooling_func=pooling_func,
        two_emb_layer=two_emb_layer,
    )


def ResNet50(feat_dim, embed_dim, pooling_func="TSTP", two_emb_layer=True):
    return ResNet(
        Bottleneck,
        [3, 4, 6, 3],
        feat_dim=feat_dim,
        embed_dim=embed_dim,
        pooling_func=pooling_func,
        two_emb_layer=two_emb_layer,
    )


def ResNet101(feat_dim, embed_dim, pooling_func="TSTP", two_emb_layer=True):
    return ResNet(
        Bottleneck,
        [3, 4, 23, 3],
        feat_dim=feat_dim,
        embed_dim=embed_dim,
        pooling_func=pooling_func,
        two_emb_layer=two_emb_layer,
    )


def ResNet152(feat_dim, embed_dim, pooling_func="TSTP", two_emb_layer=True):
    return ResNet(
        Bottleneck,
        [3, 8, 36, 3],
        feat_dim=feat_dim,
        embed_dim=embed_dim,
        pooling_func=pooling_func,
        two_emb_layer=two_emb_layer,
    )


def ResNet221(feat_dim, embed_dim, pooling_func="TSTP", two_emb_layer=True):
    return ResNet(
        Bottleneck,
        [6, 16, 48, 3],
        feat_dim=feat_dim,
        embed_dim=embed_dim,
        pooling_func=pooling_func,
        two_emb_layer=two_emb_layer,
    )


def ResNet293(feat_dim, embed_dim, pooling_func="TSTP", two_emb_layer=True):
    return ResNet(
        Bottleneck,
        [10, 20, 64, 3],
        feat_dim=feat_dim,
        embed_dim=embed_dim,
        pooling_func=pooling_func,
        two_emb_layer=two_emb_layer,
    )


if __name__ == "__main__":
    x = torch.zeros(10, 200, 80)
    model = ResNet34(feat_dim=80, embed_dim=256, pooling_func="MQMHASTP")
    model.eval()
    out = model(x)
    print(out[-1].size())

    num_params = sum(p.numel() for p in model.parameters())
    print("{} M".format(num_params / 1e6))

    # from thop import profile
    # x_np = torch.randn(1, 200, 80)
    # flops, params = profile(model, inputs=(x_np, ))
    # print("FLOPS: {} G, Params: {} M".format(flops / 1e9, params / 1e6))
