using Microsoft.Extensions.Options;
using SherpaOnnx;
using SqlSugar.IOC;
using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.IO;
using System.Linq;
using System.Text;
using System.Text.Json;
using System.Text.RegularExpressions;
using System.Threading.Tasks;
using VideoAnalysisCore.Common;
using VideoAnalysisCore.Model;
using VideoAnalysisCore.Model.Enum;
using static System.Runtime.InteropServices.JavaScript.JSType;

namespace VideoAnalysisCore.AICore.SherpaOnnx
{
    public static class SenseVoice
    {
        const string TransducerStr = "sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20";
        static OfflineRecognizer OR = default!;
        //static VoiceActivityDetector VAD = default!;
        static VadModelConfig VADModelConfig = default!;
        /// <summary>
        /// 初始化 SenseVoice
        /// </summary>
        /// <param name="numThreads">默认6线程</param>
        /// <param name="useGPU">是否使用gpu 报错请看安装CUDA环境<see cref="https://k2-fsa.github.io/sherpa/onnx/pretrained_models/whisper/large-v3.html#run-with-gpu-float32"/></param>
        public static void Init(int numThreads = 6, bool useGPU = false, bool useHotwords = false)
        {
            Console.WriteLine("初始化 SenseVoice");
            OfflineRecognizerConfig config = new OfflineRecognizerConfig();
            //采样率
            config.FeatConfig.SampleRate = 16000;
            //用于训练模型的特征维度
            config.FeatConfig.FeatureDim = 80;
            //Path to tokens.txt
            config.ModelConfig.Tokens = Path.Combine(AppCommon.AIModelFile, "sherpa-onnx-sense-voice-24-07-17", "tokens.txt");
            //SenseVoice 模型
            config.ModelConfig.SenseVoice.Model = Path.Combine(AppCommon.AIModelFile, "sherpa-onnx-sense-voice-24-07-17", "model.onnx");
            //1 使用逆文本规范化处理感官语音。
            config.ModelConfig.SenseVoice.UseInverseTextNormalization = 1;
            config.ModelConfig.SenseVoice.Language = "zh";
            //模型类型
            config.ModelConfig.ModelType = string.Empty;
            config.ModelConfig.NumThreads = numThreads;
            config.ModelConfig.Provider = "cpu";
            //需要使用GPU
            if (!useGPU)
                config.ModelConfig.Provider = "cuda";

            #region 有效的解码方法
            //贪婪搜索[greedy_search] 改进的波束搜索 [modified_beam_search]
            //贪婪搜索
            config.DecodingMethod = "greedy_search";

            ////改进的波束搜索
            //config.DecodingMethod = "modified_beam_search";
            ////仅在 --decoding--method 为 [波束搜索]modified_beam_search 时使用。
            ////它指定搜索过程中要保留的活动路径数
            //config.MaxActivePaths =4;
            #endregion

            #region  热词功能[无效]
            //if (false)
            //{
            //    //热词目录
            //    config.HotwordsFile = Path.Combine(AppCommon.AIModelFile, "Hotwords.txt");
            //    config.DecodingMethod = "modified_beam_search";
            //    //热词得分
            //    config.HotwordsScore = 1.5f;

            //    config.ModelConfig.ModelingUnit = "cjkchar+bpe";
            //    config.ModelConfig.BpeVocab = Path.Combine(AppCommon.AIModelFile, "sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20", "bpe.model");
            //    config.ModelConfig.Transducer = new OfflineTransducerModelConfig()
            //    {
            //        Decoder = Path.Combine(AppCommon.AIModelFile, "sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20", "decoder-epoch-99-avg-1.onnx"),
            //        Encoder = Path.Combine(AppCommon.AIModelFile, "sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20", "encoder-epoch-99-avg-1.onnx"),
            //        Joiner = Path.Combine(AppCommon.AIModelFile, "sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20", "joiner-epoch-99-avg-1.onnx"),
            //    };
            //}
            #endregion

            //反转文本规范化规则 fst 的路径
            config.RuleFsts = Path.Combine(AppCommon.AIModelFile, "itn_subject_sx.fst");

#if DEBUG
            config.ModelConfig.Debug = 1;
#endif

            OR = new OfflineRecognizer(config);

            VADModelConfig = new VadModelConfig();
            VADModelConfig.SileroVad.Model = Path.Combine(AppCommon.AIModelFile, "sherpa-onnx-sense-voice-24-07-17", "silero_vad.onnx");
            VADModelConfig.Debug = 0;
        }

        /// <summary>
        /// 获取语音字幕
        /// </summary>
        /// <param name="s"></param>
        /// <returns></returns>
        public static async Task<List<SenseVoiceRes>> RunTask(Stream s)
        {
            if (s is null)
                throw new Exception("音频路径 is null");
            return await TaskHandle(new WaveReader(s));


        }




        /// <summary>
        /// 获取语音字幕
        /// </summary>
        /// <param name="task"></param>
        /// <returns></returns>
        public static async Task RunTask(string task)
        {
            var filePath = Path.Combine(task.LocalPath(), "task.wav");
            if (string.IsNullOrEmpty(filePath) || !File.Exists(filePath))
                throw new Exception("task 音频路径未找到");
            await TaskHandle(new WaveReader(filePath), task);
        }

        /// <summary>
        /// 任务处理
        /// </summary>
        /// <param name="reader">Wave</param>
        /// <param name="task">任务id [默认Null]</param>
        /// <returns></returns>
        /// <exception cref="Exception"></exception>
        public static async Task<List<SenseVoiceRes>> TaskHandle(WaveReader reader, string? task = null)
        {
            if (OR is null)
                Init();
            int numSamples = reader.Samples.Length;
            int windowSize = VADModelConfig.SileroVad.WindowSize;
            int sampleRate = VADModelConfig.SampleRate;
            int numIter = numSamples / windowSize;
            var totalSecond = numSamples / (float)sampleRate;
            var res = new List<SenseVoiceRes>(500);
            using var VAD = new VoiceActivityDetector(VADModelConfig, 30);
            for (int i = 0; i != numIter; ++i)
            {
                int start = i * windowSize;
                float[] samples = new float[windowSize];
                Array.Copy(reader.Samples, start, samples, 0, windowSize);
                VAD.AcceptWaveform(samples);

                //Memory<float> samples = new float[windowSize];
                //Memory<float> sourceSpan = reader.Samples.AsMemory(start, windowSize);
                //sourceSpan.CopyTo(samples);
                //VAD.AcceptWaveform(samples.ToArray());

                //是否检测到语音
                if (VAD.IsSpeechDetected())
                {
                    //获取最新的发言片段
                    while (!VAD.IsEmpty())
                        await VAD.ReadNext(res, totalSecond, task);
                }
            }
            VAD.Flush();
            while (!VAD.IsEmpty())
                await VAD.ReadNext(res, totalSecond, task);
            //如果携带任务ID
            if (!string.IsNullOrEmpty(task))
            {
                Console.WriteLine(DateTime.Now + "=> SenseVoice 字幕数量" + res.Count);
                var captionsStr = res.ToJson();
                await DbScoped.Sugar
                    .Updateable<VideoTask>()
                    .SetColumns(it => it.Captions == captionsStr)
                    .Where(it => it.Id == long.Parse(task))
                    .ExecuteCommandAsync();
                await RedisExpand.Redis.HMSetAsync(RedisExpandKey.Task(task), "Captions", res);
                //分析完成视频字幕后继续接收任务
                RedisExpand.NewTask();
            }
            return res;
        }
        /// <summary>
        /// 处理vad 下一个切片
        /// </summary>
        /// <param name="VAD"></param>
        /// <param name="res">字幕处理后写入数组</param>
        /// <param name="totalSecond">总时长</param>
        /// <param name="task">所属任务id</param>
        /// <returns></returns>
        public static async Task ReadNext(this VoiceActivityDetector VAD, List<SenseVoiceRes> res, float totalSecond, string? task = null)
        {
            var segment = VAD.Front();
            var sampleRate = VADModelConfig.SampleRate;
            var sampleRateF = (float)VADModelConfig.SampleRate;
            float startTime = segment.Start / sampleRateF;
            float duration = segment.Samples.Length / sampleRateF;
            using var stream = OR.CreateStream();
            stream.AcceptWaveform(sampleRate, segment.Samples);
            OR.Decode(stream);
            if (!string.IsNullOrEmpty(stream.Result.Text))
            {
                var text = stream.Result.Text.Trim();
                if (text.Length == 1 &&  text == "。")// 检查字符是否只有一个句号
                {
                    VAD.Pop();
                    return;
                }
                res.Add(new()
                {
                    Text = stream.Result.Text,
                    Start = (float)Math.Round(startTime, 2, MidpointRounding.AwayFromZero),
                    End = (float)Math.Round(startTime + duration, 2, MidpointRounding.AwayFromZero),
                });
                if (!string.IsNullOrEmpty(task))
                    RedisExpand.SetTaskProgress(task, Math.Round((double)(startTime + duration) / (totalSecond) * 100,2)+"%");
            }
            VAD.Pop();
        }
    }
}