# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)

import math

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

import funasr.models.sond.pooling.pooling_layers as pooling_layers
from funasr.models.eres2net.fusion import AFF


class ReLU(nn.Hardtanh):

    def __init__(self, inplace=False):
        """Initialize ReLU.
        
            Args:
                inplace: TODO.
            """
        super(ReLU, self).__init__(0, 20, inplace)

    def __repr__(self):
        """Internal: repr  ."""
        inplace_str = "inplace" if self.inplace else ""
        return self.__class__.__name__ + " (" + inplace_str + ")"


class BasicBlockERes2NetV2(nn.Module):

    def __init__(self, in_planes, planes, stride=1, baseWidth=26, scale=2, expansion=2):
        """Initialize BasicBlockERes2NetV2.
        
            Args:
                in_planes: TODO.
                planes: TODO.
                stride: TODO.
                baseWidth: TODO.
                scale: TODO.
                expansion: TODO.
            """
        super(BasicBlockERes2NetV2, self).__init__()
        width = int(math.floor(planes * (baseWidth / 64.0)))
        self.conv1 = nn.Conv2d(in_planes, width * scale, kernel_size=1, stride=stride, bias=False)
        self.bn1 = nn.BatchNorm2d(width * scale)
        self.nums = scale
        self.expansion = expansion

        convs = []
        bns = []
        for i in range(self.nums):
            convs.append(nn.Conv2d(width, width, kernel_size=3, padding=1, bias=False))
            bns.append(nn.BatchNorm2d(width))
        self.convs = nn.ModuleList(convs)
        self.bns = nn.ModuleList(bns)
        self.relu = ReLU(inplace=True)

        self.conv3 = nn.Conv2d(width * scale, planes * self.expansion, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        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),
            )
        self.stride = stride
        self.width = width
        self.scale = scale

    def forward(self, x):
        """Forward pass for training.
        
            Args:
                x: TODO.
            """
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        spx = torch.split(out, self.width, 1)
        for i in range(self.nums):
            if i == 0:
                sp = spx[i]
            else:
                sp = sp + spx[i]
            sp = self.convs[i](sp)
            sp = self.relu(self.bns[i](sp))
            if i == 0:
                out = sp
            else:
                out = torch.cat((out, sp), 1)

        out = self.conv3(out)
        out = self.bn3(out)

        residual = self.shortcut(x)
        out += residual
        out = self.relu(out)

        return out


class BasicBlockERes2NetV2AFF(nn.Module):

    def __init__(self, in_planes, planes, stride=1, baseWidth=26, scale=2, expansion=2):
        """Initialize BasicBlockERes2NetV2AFF.
        
            Args:
                in_planes: TODO.
                planes: TODO.
                stride: TODO.
                baseWidth: TODO.
                scale: TODO.
                expansion: TODO.
            """
        super(BasicBlockERes2NetV2AFF, self).__init__()
        width = int(math.floor(planes * (baseWidth / 64.0)))
        self.conv1 = nn.Conv2d(in_planes, width * scale, kernel_size=1, stride=stride, bias=False)
        self.bn1 = nn.BatchNorm2d(width * scale)
        self.nums = scale
        self.expansion = expansion

        convs = []
        fuse_models = []
        bns = []
        for i in range(self.nums):
            convs.append(nn.Conv2d(width, width, kernel_size=3, padding=1, bias=False))
            bns.append(nn.BatchNorm2d(width))
        for j in range(self.nums - 1):
            fuse_models.append(AFF(channels=width, r=4))

        self.convs = nn.ModuleList(convs)
        self.bns = nn.ModuleList(bns)
        self.fuse_models = nn.ModuleList(fuse_models)
        self.relu = ReLU(inplace=True)

        self.conv3 = nn.Conv2d(width * scale, planes * self.expansion, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        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),
            )
        self.stride = stride
        self.width = width
        self.scale = scale

    def forward(self, x):
        """Forward pass for training.
        
            Args:
                x: TODO.
            """
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        spx = torch.split(out, self.width, 1)
        for i in range(self.nums):
            if i == 0:
                sp = spx[i]
            else:
                sp = self.fuse_models[i - 1](sp, spx[i])
            sp = self.convs[i](sp)
            sp = self.relu(self.bns[i](sp))
            if i == 0:
                out = sp
            else:
                out = torch.cat((out, sp), 1)

        out = self.conv3(out)
        out = self.bn3(out)

        residual = self.shortcut(x)
        out += residual
        out = self.relu(out)

        return out


class ERes2NetV2(nn.Module):
    def __init__(
        self,
        block=BasicBlockERes2NetV2,
        block_fuse=BasicBlockERes2NetV2AFF,
        num_blocks=[3, 4, 6, 3],
        m_channels=64,
        feat_dim=80,
        embedding_size=192,
        baseWidth=26,
        scale=2,
        expansion=2,
        pooling_func="TSTP",
        two_emb_layer=False,
    ):
        """Initialize ERes2NetV2.
        
            Args:
                block: TODO.
                block_fuse: TODO.
                num_blocks: TODO.
                m_channels: TODO.
                feat_dim: Size/dimension parameter.
                embedding_size: Size/dimension parameter.
                baseWidth: TODO.
                scale: TODO.
                expansion: TODO.
                pooling_func: TODO.
                two_emb_layer: TODO.
            """
        super(ERes2NetV2, self).__init__()
        self.in_planes = m_channels
        self.feat_dim = feat_dim
        self.embedding_size = embedding_size
        self.stats_dim = int(feat_dim / 8) * m_channels * 8
        self.two_emb_layer = two_emb_layer
        self.baseWidth = baseWidth
        self.scale = scale
        self.expansion = expansion

        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_fuse, m_channels * 4, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block_fuse, m_channels * 8, num_blocks[3], stride=2)

        self.layer3_ds = nn.Conv2d(
            m_channels * 4 * self.expansion, m_channels * 8 * self.expansion,
            kernel_size=3, padding=1, stride=2, bias=False,
        )
        self.fuse34 = AFF(channels=m_channels * 8 * self.expansion, r=4)

        self.n_stats = 1 if pooling_func == "TAP" or pooling_func == "TSDP" else 2
        self.pool = getattr(pooling_layers, pooling_func)(in_dim=self.stats_dim * self.expansion)
        self.seg_1 = nn.Linear(self.stats_dim * self.expansion * self.n_stats, embedding_size)
        if self.two_emb_layer:
            self.seg_bn_1 = nn.BatchNorm1d(embedding_size, affine=False)
            self.seg_2 = nn.Linear(embedding_size, embedding_size)
        else:
            self.seg_bn_1 = nn.Identity()
            self.seg_2 = nn.Identity()

    def _make_layer(self, block, planes, num_blocks, stride):
        """Internal: make layer.
        
            Args:
                block: TODO.
                planes: TODO.
                num_blocks: TODO.
                stride: TODO.
            """
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(
                block(self.in_planes, planes, stride, baseWidth=self.baseWidth, scale=self.scale, expansion=self.expansion)
            )
            self.in_planes = planes * self.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        """Forward pass for training.
        
            Args:
                x: TODO.
            """
        x = x.permute(0, 2, 1)  # (B,T,F) => (B,F,T)
        x = x.unsqueeze_(1)
        out = F.relu(self.bn1(self.conv1(x)))
        out1 = self.layer1(out)
        out2 = self.layer2(out1)
        out3 = self.layer3(out2)
        out4 = self.layer4(out3)
        out3_ds = self.layer3_ds(out3)
        fuse_out34 = self.fuse34(out4, out3_ds)
        stats = self.pool(fuse_out34)

        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_b
        else:
            return embed_a