# 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)

""" Res2Net implementation is adapted from https://github.com/wenet-e2e/wespeaker.
    ERes2Net incorporates both local and global feature fusion techniques to improve the performance.
    The local feature fusion (LFF) fuses the features within one single residual block to extract the local signal.
    The global feature fusion (GFF) takes acoustic features of different scales as input to aggregate global signal.
    ERes2Net-Large is an upgraded version of ERes2Net that uses a larger number of parameters to achieve better
    recognition performance. Parameters expansion, baseWidth, and scale can be modified to obtain optimal performance.
"""

import torch
import math
import torch.nn as nn
import torch.nn.functional as F
import funasr.models.whisper_lid.eres2net.pooling_layers as pooling_layers
from funasr.models.whisper_lid.eres2net.fusion import AFF


class ReLU(nn.Hardtanh):

    def __init__(self, inplace=False):
        super(ReLU, self).__init__(0, 20, inplace)

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


def conv1x1(in_planes, out_planes, stride=1):
    "1x1 convolution without padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False)


def conv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)


class BasicBlockERes2Net(nn.Module):
    expansion = 2

    def __init__(self, in_planes, planes, stride=1, baseWidth=32, scale=2):
        super(BasicBlockERes2Net, self).__init__()
        width = int(math.floor(planes * (baseWidth / 64.0)))
        self.conv1 = conv1x1(in_planes, width * scale, stride)
        self.bn1 = nn.BatchNorm2d(width * scale)
        self.nums = scale

        convs = []
        bns = []
        for i in range(self.nums):
            convs.append(conv3x3(width, width))
            bns.append(nn.BatchNorm2d(width))
        self.convs = nn.ModuleList(convs)
        self.bns = nn.ModuleList(bns)
        self.relu = ReLU(inplace=True)

        self.conv3 = conv1x1(width * scale, planes * self.expansion)
        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):
        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 BasicBlockERes2Net_diff_AFF(nn.Module):
    expansion = 2

    def __init__(self, in_planes, planes, stride=1, baseWidth=32, scale=2):
        super(BasicBlockERes2Net_diff_AFF, self).__init__()
        width = int(math.floor(planes * (baseWidth / 64.0)))
        self.conv1 = conv1x1(in_planes, width * scale, stride)
        self.bn1 = nn.BatchNorm2d(width * scale)
        self.nums = scale

        convs = []
        fuse_models = []
        bns = []
        for i in range(self.nums):
            convs.append(conv3x3(width, width))
            bns.append(nn.BatchNorm2d(width))
        for j in range(self.nums - 1):
            fuse_models.append(AFF(channels=width))

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

        self.conv3 = conv1x1(width * scale, planes * self.expansion)
        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):
        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 ERes2Net(nn.Module):
    def __init__(
        self,
        block=BasicBlockERes2Net,
        block_fuse=BasicBlockERes2Net_diff_AFF,
        num_blocks=[3, 4, 6, 3],
        m_channels=32,
        feat_dim=80,
        embedding_size=192,
        pooling_func="TSTP",
        two_emb_layer=False,
    ):
        super(ERes2Net, 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._output_size = embedding_size

        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)

        # Downsampling module for each layer
        self.layer1_downsample = nn.Conv2d(
            m_channels * 2, m_channels * 4, kernel_size=3, stride=2, padding=1, bias=False
        )
        self.layer2_downsample = nn.Conv2d(
            m_channels * 4, m_channels * 8, kernel_size=3, padding=1, stride=2, bias=False
        )
        self.layer3_downsample = nn.Conv2d(
            m_channels * 8, m_channels * 16, kernel_size=3, padding=1, stride=2, bias=False
        )

        # Bottom-up fusion module
        self.fuse_mode12 = AFF(channels=m_channels * 4)
        self.fuse_mode123 = AFF(channels=m_channels * 8)
        self.fuse_mode1234 = AFF(channels=m_channels * 16)

        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 * block.expansion)
        self.seg_1 = nn.Linear(self.stats_dim * block.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):
        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 output_size(self) -> int:
        return self._output_size

    def forward(self, x, ilens):
        # assert x.shape[1] == ilens.max()
        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)
        out1_downsample = self.layer1_downsample(out1)
        fuse_out12 = self.fuse_mode12(out2, out1_downsample)
        out3 = self.layer3(out2)
        fuse_out12_downsample = self.layer2_downsample(fuse_out12)
        fuse_out123 = self.fuse_mode123(out3, fuse_out12_downsample)
        out4 = self.layer4(out3)
        fuse_out123_downsample = self.layer3_downsample(fuse_out123)
        fuse_out1234 = self.fuse_mode1234(out4, fuse_out123_downsample)
        olens = (((((ilens - 1) // 2 + 1) - 1) // 2 + 1) - 1) // 2 + 1
        stats = self.pool(fuse_out1234, olens)

        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


class BasicBlockRes2Net(nn.Module):
    expansion = 2

    def __init__(self, in_planes, planes, stride=1, baseWidth=32, scale=2):
        super(BasicBlockRes2Net, self).__init__()
        width = int(math.floor(planes * (baseWidth / 64.0)))
        self.conv1 = conv1x1(in_planes, width * scale, stride)
        self.bn1 = nn.BatchNorm2d(width * scale)
        self.nums = scale - 1
        convs = []
        bns = []
        for i in range(self.nums):
            convs.append(conv3x3(width, width))
            bns.append(nn.BatchNorm2d(width))
        self.convs = nn.ModuleList(convs)
        self.bns = nn.ModuleList(bns)
        self.relu = ReLU(inplace=True)

        self.conv3 = conv1x1(width * scale, planes * self.expansion)
        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):
        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 = torch.cat((out, spx[self.nums]), 1)

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

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

        return out


class Res2Net(nn.Module):
    def __init__(
        self,
        block=BasicBlockRes2Net,
        num_blocks=[3, 4, 6, 3],
        m_channels=32,
        feat_dim=80,
        embedding_size=192,
        pooling_func="TSTP",
        two_emb_layer=False,
    ):
        super(Res2Net, 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.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.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 * block.expansion)
        self.seg_1 = nn.Linear(self.stats_dim * block.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):
        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_b
        else:
            return embed_a
