#!/usr/bin/env python3
# Copyright    2026  Xiaomi Corp.        (authors:  Han Zhu)
#
# See ../../LICENSE for clarification regarding multiple authors
#
# 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.

import os

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


class ECAPA_TDNN_WAVLM(nn.Module):
    def __init__(
        self,
        feat_dim=80,
        channels=512,
        emb_dim=192,
        global_context_att=False,
        sr=16000,
        ssl_model_path=None,
    ):
        super().__init__()
        self.sr = sr

        if ssl_model_path is None:
            self.feature_extract = torch.hub.load("s3prl/s3prl", "wavlm_large")
        else:
            self.feature_extract = torch.hub.load(
                os.path.dirname(ssl_model_path),
                "wavlm_local",
                source="local",
                ckpt=os.path.join(ssl_model_path, "wavlm_large.pt"),
            )

        if len(self.feature_extract.model.encoder.layers) == 24 and hasattr(
            self.feature_extract.model.encoder.layers[23].self_attn,
            "fp32_attention",
        ):
            self.feature_extract.model.encoder.layers[
                23
            ].self_attn.fp32_attention = False
        if len(self.feature_extract.model.encoder.layers) == 24 and hasattr(
            self.feature_extract.model.encoder.layers[11].self_attn,
            "fp32_attention",
        ):
            self.feature_extract.model.encoder.layers[
                11
            ].self_attn.fp32_attention = False

        self.feat_num = self.get_feat_num()
        self.feature_weight = nn.Parameter(torch.zeros(self.feat_num))

        self.instance_norm = nn.InstanceNorm1d(feat_dim)
        # self.channels = [channels] * 4 + [channels * 3]
        self.channels = [channels] * 4 + [1536]

        self.layer1 = Conv1dReluBn(feat_dim, self.channels[0], kernel_size=5, padding=2)
        self.layer2 = SE_Res2Block(
            self.channels[0],
            self.channels[1],
            kernel_size=3,
            stride=1,
            padding=2,
            dilation=2,
            scale=8,
            se_bottleneck_dim=128,
        )
        self.layer3 = SE_Res2Block(
            self.channels[1],
            self.channels[2],
            kernel_size=3,
            stride=1,
            padding=3,
            dilation=3,
            scale=8,
            se_bottleneck_dim=128,
        )
        self.layer4 = SE_Res2Block(
            self.channels[2],
            self.channels[3],
            kernel_size=3,
            stride=1,
            padding=4,
            dilation=4,
            scale=8,
            se_bottleneck_dim=128,
        )

        # self.conv = nn.Conv1d(self.channels[-1], self.channels[-1], kernel_size=1)
        cat_channels = channels * 3
        self.conv = nn.Conv1d(cat_channels, self.channels[-1], kernel_size=1)
        self.pooling = AttentiveStatsPool(
            self.channels[-1],
            attention_channels=128,
            global_context_att=global_context_att,
        )
        self.bn = nn.BatchNorm1d(self.channels[-1] * 2)
        self.linear = nn.Linear(self.channels[-1] * 2, emb_dim)

    def get_feat_num(self):
        self.feature_extract.eval()
        wav = [torch.randn(self.sr).to(next(self.feature_extract.parameters()).device)]
        with torch.no_grad():
            features = self.feature_extract(wav)
        select_feature = features["hidden_states"]
        if isinstance(select_feature, (list, tuple)):
            return len(select_feature)
        else:
            return 1

    def get_feat(self, x):
        with torch.no_grad():
            x = self.feature_extract([sample for sample in x])

        x = x["hidden_states"]
        if isinstance(x, (list, tuple)):
            x = torch.stack(x, dim=0)
        else:
            x = x.unsqueeze(0)
        norm_weights = (
            F.softmax(self.feature_weight, dim=-1)
            .unsqueeze(-1)
            .unsqueeze(-1)
            .unsqueeze(-1)
        )
        x = (norm_weights * x).sum(dim=0)
        x = torch.transpose(x, 1, 2) + 1e-6

        x = self.instance_norm(x)
        return x

    def forward(self, x):
        x = self.get_feat(x)

        out1 = self.layer1(x)
        out2 = self.layer2(out1)
        out3 = self.layer3(out2)
        out4 = self.layer4(out3)

        out = torch.cat([out2, out3, out4], dim=1)
        out = F.relu(self.conv(out))
        out = self.bn(self.pooling(out))
        out = self.linear(out)

        return out


# part of the code is borrowed from https://github.com/lawlict/ECAPA-TDNN

""" Res2Conv1d + BatchNorm1d + ReLU
"""


class Res2Conv1dReluBn(nn.Module):
    """
    in_channels == out_channels == channels
    """

    def __init__(
        self,
        channels,
        kernel_size=1,
        stride=1,
        padding=0,
        dilation=1,
        bias=True,
        scale=4,
    ):
        super().__init__()
        assert channels % scale == 0, "{} % {} != 0".format(channels, scale)
        self.scale = scale
        self.width = channels // scale
        self.nums = scale if scale == 1 else scale - 1

        self.convs = []
        self.bns = []
        for i in range(self.nums):
            self.convs.append(
                nn.Conv1d(
                    self.width,
                    self.width,
                    kernel_size,
                    stride,
                    padding,
                    dilation,
                    bias=bias,
                )
            )
            self.bns.append(nn.BatchNorm1d(self.width))
        self.convs = nn.ModuleList(self.convs)
        self.bns = nn.ModuleList(self.bns)

    def forward(self, x):
        out = []
        spx = torch.split(x, self.width, 1)
        for i in range(self.nums):
            if i == 0:
                sp = spx[i]
            else:
                sp = sp + spx[i]
            # Order: conv -> relu -> bn
            sp = self.convs[i](sp)
            sp = self.bns[i](F.relu(sp))
            out.append(sp)
        if self.scale != 1:
            out.append(spx[self.nums])
        out = torch.cat(out, dim=1)

        return out


""" Conv1d + BatchNorm1d + ReLU
"""


class Conv1dReluBn(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size=1,
        stride=1,
        padding=0,
        dilation=1,
        bias=True,
    ):
        super().__init__()
        self.conv = nn.Conv1d(
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            dilation,
            bias=bias,
        )
        self.bn = nn.BatchNorm1d(out_channels)

    def forward(self, x):
        return self.bn(F.relu(self.conv(x)))


""" The SE connection of 1D case.
"""


class SE_Connect(nn.Module):
    def __init__(self, channels, se_bottleneck_dim=128):
        super().__init__()
        self.linear1 = nn.Linear(channels, se_bottleneck_dim)
        self.linear2 = nn.Linear(se_bottleneck_dim, channels)

    def forward(self, x):
        out = x.mean(dim=2)
        out = F.relu(self.linear1(out))
        out = torch.sigmoid(self.linear2(out))
        out = x * out.unsqueeze(2)

        return out


""" SE-Res2Block of the ECAPA-TDNN architecture.
"""


# def SE_Res2Block(channels, kernel_size, stride, padding, dilation, scale):
#     return nn.Sequential(
#         Conv1dReluBn(channels, 512, kernel_size=1, stride=1, padding=0),
#         Res2Conv1dReluBn(512, kernel_size, stride, padding, dilation, scale=scale),
#         Conv1dReluBn(512, channels, kernel_size=1, stride=1, padding=0),
#         SE_Connect(channels)
#     )


class SE_Res2Block(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        stride,
        padding,
        dilation,
        scale,
        se_bottleneck_dim,
    ):
        super().__init__()
        self.Conv1dReluBn1 = Conv1dReluBn(
            in_channels, out_channels, kernel_size=1, stride=1, padding=0
        )
        self.Res2Conv1dReluBn = Res2Conv1dReluBn(
            out_channels, kernel_size, stride, padding, dilation, scale=scale
        )
        self.Conv1dReluBn2 = Conv1dReluBn(
            out_channels, out_channels, kernel_size=1, stride=1, padding=0
        )
        self.SE_Connect = SE_Connect(out_channels, se_bottleneck_dim)

        self.shortcut = None
        if in_channels != out_channels:
            self.shortcut = nn.Conv1d(
                in_channels=in_channels,
                out_channels=out_channels,
                kernel_size=1,
            )

    def forward(self, x):
        residual = x
        if self.shortcut:
            residual = self.shortcut(x)

        x = self.Conv1dReluBn1(x)
        x = self.Res2Conv1dReluBn(x)
        x = self.Conv1dReluBn2(x)
        x = self.SE_Connect(x)

        return x + residual


""" Attentive weighted mean and standard deviation pooling.
"""


class AttentiveStatsPool(nn.Module):
    def __init__(self, in_dim, attention_channels=128, global_context_att=False):
        super().__init__()
        self.global_context_att = global_context_att

        # Use Conv1d with stride == 1 rather than Linear,
        #  then we don't need to transpose inputs.
        if global_context_att:
            self.linear1 = nn.Conv1d(
                in_dim * 3, attention_channels, kernel_size=1
            )  # equals W and b in the paper
        else:
            self.linear1 = nn.Conv1d(
                in_dim, attention_channels, kernel_size=1
            )  # equals W and b in the paper
        self.linear2 = nn.Conv1d(
            attention_channels, in_dim, kernel_size=1
        )  # equals V and k in the paper

    def forward(self, x):

        if self.global_context_att:
            context_mean = torch.mean(x, dim=-1, keepdim=True).expand_as(x)
            context_std = torch.sqrt(
                torch.var(x, dim=-1, keepdim=True) + 1e-10
            ).expand_as(x)
            x_in = torch.cat((x, context_mean, context_std), dim=1)
        else:
            x_in = x

        # DON'T use ReLU here! In experiments, I find ReLU hard to converge.
        alpha = torch.tanh(self.linear1(x_in))
        # alpha = F.relu(self.linear1(x_in))
        alpha = torch.softmax(self.linear2(alpha), dim=2)
        mean = torch.sum(alpha * x, dim=2)
        residuals = torch.sum(alpha * (x**2), dim=2) - mean**2
        std = torch.sqrt(residuals.clamp(min=1e-9))
        return torch.cat([mean, std], dim=1)