aha 0.2.6

// use std::io::Cursor;

// use std::fs::File;
// use symphonia::core::io::MediaSourceStream;
// use std::io::{Read, Seek};
// use std::{io::Cursor, time::Instant};

// use aha::utils::tensor_utils::get_mask_from_lengths;
// use aha::utils::tensor_utils::repeat_interleave;
// use crate::params::chat::ChatCompletionParameters;
use anyhow::Result;
// use candle_core::Tensor;
// use kaldi_native_fbank::{
//     FbankComputer, FbankOptions,
//     window::{Window, extract_window},
// };
// use byteorder::{LittleEndian, ReadBytesExt};
// use candle_core::Tensor;
use modelscope::{DownloadOptions, ModelScope};
// use sentencepiece::SentencePieceProcessor;
// use zip::ZipArchive;

#[tokio::test]
async fn download_test() -> Result<()> {
    // cargo test -F cuda --test messy_test download_test -r -- --nocapture
    let model_id = "unsloth/Qwen3.5-4B-GGUF";
    let model_name = "Qwen3.5-4B-IQ4_NL.gguf";
    let save_dir =
        aha::utils::get_default_save_dir().ok_or(anyhow::anyhow!("Failed to get save dir"))?;
    let _ = ModelScope::download_with_options(
        model_id,
        save_dir,
        DownloadOptions {
            files: (vec![model_name.to_string()]).into(),
        },
    )
    .await;
    Ok(())
}

#[test]
fn messy_test() -> Result<()> {
    // RUST_BACKTRACE=1 cargo test -F cuda --test messy_test messy_test -r -- --nocapture
    // let device = aha::Device::Cpu;
    let device = aha::Device::new_cuda(0)?;
    println!("{:?}", device);
    // let input = Tensor::new(&[5u32, 4, 3, 6], &device)?;
    // let mask = get_mask_from_lengths(&input)?;
    // println!("{}", mask);
    // let audio_path = "file:///home/jhq/python_code/FireRedASR2S/assets/hello_zh.wav";
    // let device = aha::Device::Cpu;
    // let wave = load_audio_with_resample(audio_path, &device, Some(16000), true)?;
    // println!("len: {}", wave);
    // let wave = wave.squeeze(0)?.to_vec1::<f32>()?;
    // println!("wave len: {}", wave.len());
    // let mut opts = FbankOptions::default();
    // opts.frame_opts.dither = 0.0;
    // opts.frame_opts.samp_freq = 16000.;
    // opts.frame_opts.frame_length_ms = 25.;
    // opts.frame_opts.frame_shift_ms = 10.;
    // opts.frame_opts.snip_edges = true;
    // opts.mel_opts.num_bins = 80;
    // opts.mel_opts.debug_mel = false;
    // opts.use_energy = false;

    // let mut comp =
    //     FbankComputer::new(opts.clone()).map_err(|e| anyhow!("fbank comput err: {e}"))?;
    // let win = Window::new(&opts.frame_opts).unwrap();
    // let padded = opts.frame_opts.padded_window_size();

    // let mut feats = vec![];
    // let mut window_buf = vec![0.0; padded];
    // for frame in 0..230 {
    //     let raw_log_energy = extract_window(
    //         0,
    //         &wave,
    //         frame,
    //         &opts.frame_opts,
    //         Some(&win),
    //         &mut window_buf,
    //     )
    //     .unwrap();
    //     let mut feat = vec![0.0; comp.dim()];
    //     comp.compute(raw_log_energy, 1.0, &mut window_buf, &mut feat);
    //     feats.push(feat);
    // }
    // let feats = Tensor::new(feats, &aha::Device::Cpu)?;
    // println!("feats: {}", feats);
    // println!("wave len: {}", wave.len());
    // let mut feats = vec![];
    // let frame_num = (wave.len() + padded - 1) / padded;
    // for i in 0..frame_num {
    //     let mut window_buf = vec![0.0; padded];
    //     let raw_log_energy =
    //         extract_window(0, &wave, i, &opts.frame_opts, Some(&win), &mut window_buf)
    //             .map_err(|_| anyhow!("extract_window  err"))?;
    //     let mut feat = vec![0.0; comp.dim()];
    //     comp.compute(raw_log_energy, 1.0, &mut window_buf, &mut feat);
    //     feats.push(feat);
    // }
    // let feats = Tensor::new(feats, &aha::Device::Cpu)?;
    // println!("feats: {:?}", feats);
    // let mut window_buf = vec![0.0; padded];
    // println!("window_buf len: {}", window_buf.len());
    // let raw_log_energy = extract_window(0, &wave, 0, &opts.frame_opts, Some(&win), &mut window_buf)
    //     .map_err(|_| anyhow!("extract_window  err"))?;

    // let mut feat = vec![0.0; comp.dim()];
    // println!("feat len: {}", feat.len());
    // comp.compute(raw_log_energy, 1.0, &mut window_buf, &mut feat);
    // println!("{feat:?}");
    // let model = WhichModel::LFM2_1_2B;
    // println!("model: {:?}, model_id: {}", model, model.as_string());
    // let model_list = WhichModel::model_list();
    // println!("model_list: {:#?}", model_list);
    // println!("当前秒级时间戳: {}", timestamp());
    // println!("当前毫秒级时间戳: {}", timestamp_millis());

    // let t1 = Tensor::randn(0.0, 1.0, (1, 2, 6), device)?;
    // println!(" t1: {}", t1);
    // let t2 = t1.pad_with_zeros(D::Minus1, -3, 0)?;
    // println!(" t2: {}", t);
    // let save_dir =
    //     aha::utils::get_default_save_dir().ok_or(anyhow::anyhow!("Failed to get save dir"))?;
    // let model_path = format!("{}/deepseek-ai/DeepSeek-OCR-2/", save_dir);
    // let stem = std::path::Path::new(&model_path)
    //     .file_name()
    //     .and_then(|s| s.to_str())
    //     .unwrap_or("qwen3.5");
    // println!("stem: {:?}", stem);
    // let device = &candle_core::Device::Cpu;
    // let t1 = Tensor::randn(0.0, 1.0, (16, 9, 64, 128), device)?;
    // let t2 = Tensor::randn(0.0, 1.0, (16, 9, 128, 64), device)?;
    // let out = t1.matmul(&t2)?;
    // println!("out shape: {:?}", out);

    // let input = Tensor::arange(0.0f32, 25.0f32, device)?.reshape((5, 5))?;
    // println!("input: {}", input);
    // // let input = input.unsqueeze(D::Minus1)?;
    // // let input = input.repeat((1, 1, 2))?;
    // // let input = input.flatten(D::Minus2, D::Minus1)?;
    // let output = repeat_interleave(&input, 2, 1)?;
    // println!("output: {}", output);

    // let x_nearest = interpolate_nearest_2d(&input, (10, 10))?;
    // println!("x_nearest: {}", x_nearest);
    // let input = Tensor::arange(0.0f32, 25.0f32, device)?.reshape((1, 5, 5))?;
    // println!("input: {}", input);
    // let x_nearest = interpolate_nearest_1d(&input, 10)?;
    // println!("x_nearest: {}", x_nearest);
    // let save_dir: String =
    //     aha::utils::get_default_save_dir().ok_or(anyhow::anyhow!("Failed to get save dir"))?;
    // let model_path = format!("{}/IndexTeam/IndexTTS-2/", save_dir);
    // let emo_matrix_path = model_path.clone() + "/feat2.pt";
    // let t_emo = load_tensor_from_pt(
    //     &emo_matrix_path,
    //     "feat2/data/0",
    //     Shape::from_dims(&[73, 1280]),
    //     device,
    // )?;
    // println!("t_emo: {}", t_emo);
    // let skp_matrix_path = model_path + "/feat1.pt";
    // let t_skp = load_tensor_from_pt(
    //     &skp_matrix_path,
    //     "feat1/data/0",
    //     Shape::from_dims(&[73, 192]),
    //     device,
    // )?;
    // println!("t_skp: {}", t_skp);
    // let file = File::open(emo_matrix_path)?;
    // let mut archive = ZipArchive::new(file)?;
    // // 列出所有文件（调试用）
    // for i in 0..archive.len() {
    //     let file = archive.by_index(i)?;
    //     println!("File: {} ({} bytes)", file.name(), file.size());
    // }
    // // 读取原始字节数据
    // let mut data_file = archive.by_name("feat2/data/0")?;
    // let mut buffer = Vec::new();
    // data_file.read_to_end(&mut buffer)?;
    // // 将字节转换为 f32 (little endian)
    // let mut cursor = Cursor::new(buffer);
    // let num_elements = 73 * 1280; // 93,440
    // let mut data = Vec::with_capacity(num_elements);

    // for _ in 0..num_elements {
    //     let val = cursor.read_f32::<LittleEndian>()?;
    //     data.push(val);
    // }
    // let t = Tensor::from_vec(data, (73, 1280), device)?;
    // println!("t: {}", t);
    // let message = r#"
    // {
    //     "model": "index-tts2",
    //     "messages": [
    //         {
    //             "role": "user",
    //             "content": [
    //                 {
    //                     "type": "audio",
    //                     "audio_url":
    //                     {
    //                         "url": "file:///home/jhq/Videos/voice_01.wav"
    //                     }
    //                 },
    //                 {
    //                     "type": "text",
    //                     "text": "你好啊"
    //                 }
    //             ]
    //         }
    //     ],
    //     "metadata": {"emo_vector": "[0, 0, 0, 0, 0, 0, 0.45, 0]"}
    // }
    // "#;
    // let mes: ChatCompletionParameters = serde_json::from_str(message)?;

    // if let Some(map) = &mes.metadata
    //     && let Some(emo_vector_str) = map.get("emo_vector")
    // {
    //     match serde_json::from_str::<Vec<f32>>(emo_vector_str) {
    //         Ok(emo_vector) => {
    //             println!("Parsed emo_vector: {:?}", emo_vector);
    //             // 现在 emo_vector 是 Vec<f32>: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.45, 0.0]
    //         }
    //         Err(e) => {
    //             eprintln!("Failed to parse emo_vector: {}", e);
    //         }
    //     }
    // }
    // let save_dir =
    //     aha::utils::get_default_save_dir().ok_or(anyhow::anyhow!("Failed to get save dir"))?;
    // let model_path = format!("{}/IndexTeam/IndexTTS-2", save_dir);
    // let bpe_path = model_path.to_string() + "/bpe.model";
    // let tokenizer = SentencePieceProcessor::open(bpe_path)
    //     .map_err(|e| anyhow!(format!("load bpe.model file error:{}", e)))?;
    // let tokens = tokenizer
    //     .encode("你好啊")
    //     .map_err(|e| anyhow!(format!("tokenizer encode error:{}", e)))?;
    // println!("tokens: {:?}", tokens);
    // let t = Tensor::arange(0.0f32, 40.0, device)?.broadcast_as((1, 40, 40))?;
    // println!("t: {}", t);
    // let i_start = Instant::now();
    // let t_inter = interpolate_nearest_1d(&t, 20)?;
    // let i_duration = i_start.elapsed();
    // println!("Time elapsed in interpolate_nearest_1d is: {:?}", i_duration);
    // println!("t_inter: {}", t_inter);
    // let url = "https://sis-sample-audio.obs.cn-north-1.myhuaweicloud.com/16k16bit.mp3";
    // let client = reqwest::blocking::Client::new();
    // let response = client.get(url).send()?;
    // let vec_u8 = response.bytes()?.to_vec();
    // let mut content = Cursor::new(vec_u8);
    // let mss = MediaSourceStream::new(Box::new(content), Default::default());
    // let window = create_hann_window(400, DType::F32, device)?;
    // println!("window: {}", window);
    // let audio_path = "file:///home/jhq/Videos/voice_01.wav";
    // let audio_path = "/home/jhq/Videos/zh.mp3";
    // let audio_path = "/home/jhq/Videos/zh.mp3";
    // // let audio_tensor = load_and_resample_audio_rubato(audio_path, 16000, device)?;
    // // let audio_tensor = load_audio_with_resample(audio_path, device, Some(16000))?;
    // // println!("audio_tensor: {}", audio_tensor);
    // #[cfg(feature = "ffmpeg")]
    // {
    //     use aha::utils::audio_utils::load_and_resample_audio_ffmpeg;

    //     let audio_tensor = load_and_resample_audio_ffmpeg(audio_path, Some(16000), device)?;
    //     println!("audio_tensor: {}", audio_tensor);
    // }

    // // let path = get_default_save_dir();
    // // let x = Tensor::new(array, device)
    // let x = Tensor::arange(0.0, 9.0, device)?;
    // println!("x: {}", x);
    // let x = x
    //     .unsqueeze(0)?
    //     .unsqueeze(0)?
    //     .broadcast_as((5, 5, 9))?
    //     .reshape((5, 5, 3, 3))?;
    // println!("x: {}", x);
    // let x = x.permute((0, 2, 1, 3))?;
    // println!("x: {}", x);
    // let x = x.reshape((15, 15))?;
    // println!("x: {}", x);
    // let xs = Tensor::rand(0.0, 5.0, (1, 1, 3, 3), device)?;
    // println!("xs: {}", xs);
    // let xs = xs.pad_with_zeros(3, 2, 2)?
    //             .pad_with_zeros(2, 2, 2)?;
    // println!("xs: {}", xs);
    // let xs = Tensor::arange(0.0, 25.0, device)?;
    // println!("xs: {}", xs);
    // let splits = split_tensor_with_size(&xs, 5, 0)?;
    // for v in splits {
    //     println!("v: {}", v);
    // }
    // let xs = Tensor::arange(0.0, 25.0, device)?.broadcast_as((1, 1, 5, 5))?;
    // println!("xs: {}", xs);
    // let xs = xs.avg_pool2d(5)?;
    // println!("xs: {}", xs);
    // let xs = Tensor::rand(0.0, 1.0, (1, 4, 4, 2), device)?;
    // println!("xs: {}", xs);
    // let shape = Shape::from_dims(&[1, 2, 2, 2, 2, 2]);
    // let xs = xs.reshape(shape)?;
    // println!("xs: {}", xs);
    // let x0 = xs.i((.., .., 0, .., 0, ..))?;
    // let x1 = xs.i((.., .., 1, .., 0, ..))?;
    // let x2 = xs.i((.., .., 0, .., 1, ..))?;
    // let x3 = xs.i((.., .., 1, .., 1, ..))?;
    // let xs = Tensor::cat(&[x0, x1, x2, x3], D::Minus1)?;
    // println!("xs: {}", xs);
    // let xs = xs.reshape((1, (), 4 * 2))?;
    // println!("xs: {}", xs);
    // let path_str = "file://./assets/img/ocr_test1.png";
    // let path = url::Url::from_str(path_str)?;
    // let path = path.to_file_path();
    // let path = match path {
    //     Ok(path) => path,
    //     Err(_) => {
    //         let mut path = path_str.to_owned();
    //         path = path.split_off(7);
    //         PathBuf::from(path)
    //     }
    // };
    // println!("to file path: {:?}", path);

    // let device = &candle_core::Device::Cpu;
    // let t = Tensor::arange(0.0f32, 40.0, device)?.broadcast_as((1, 1, 40, 40))?;
    // println!("t: {}", t);
    // let i_start = Instant::now();
    // let t_inter = interpolate_bilinear(&t, (20, 20), Some(false))?;
    // let i_duration = i_start.elapsed();
    // println!("Time elapsed in interpolate_bilinear is: {:?}", i_duration);
    // println!("t_inter: {}", t_inter);
    // let x: Vec<u32> = (0..5).flat_map(|_| 0u32..10).collect();
    // let id: Vec<u32> = (0..5).flat_map(|h| vec![h; 10]).collect();
    // println!("x: {:?}", id);
    // let t = Tensor::randn(0.0f32, 1.0, (1, 768, 64, 64), device)?;
    // let t = Tensor::arange(0u32, 10, device)?.broadcast_as((1, 10))?;
    // let eq = t.broadcast_eq(&Tensor::new(5u32, device)?)?;
    // println!("eq: {}", eq);
    // let t = Tensor::arange(0.0f32, 10.0, device)?.broadcast_as((1, 1, 10, 10))?;
    // println!("t: {}", t);
    // let t_resized = interpolate_bicubic(&t, (5, 5), Some(true), Some(false))?;
    // println!("t_resized: {}", t_resized);
    // let t1 = Tensor::rand(0.0, 1.0, (1, 5, 5, 10), device)?;
    // let t2 = Tensor::rand(0.0, 1.0, (5, 8, 10), device)?;
    // let t2 = t2.t()?;
    // println!("t2: {:?}", t2);
    // let re = t1.broadcast_matmul(&t2)?;
    // println!("re: {:?}", re);
    // let index = Tensor::arange(0u32, 10u32, device)?;
    // let index_2d_vec = vec![index;5];
    // let index_2d = Tensor::stack(&index_2d_vec, 0)?;
    // println!("index_2d: {}", index_2d);
    // let t = Tensor::rand(0.0, 1.0, (20, 8), device)?;
    // println!("t: {}", t);
    // let res = index_select_2d(&t, &index_2d)?;
    // println!("res: {}", res);
    // let t = Tensor::arange(0.0, 10.0, device)?
    //     .unsqueeze(0)?
    //     .unsqueeze(0)?;
    // println!("t: {}", t);
    // let t_resized = interpolate_linear(&t, 20, None)?;
    // println!("t_resized: {}", t_resized);

    // let grid_thw = Tensor::new(vec![vec![3u32, 12, 20], vec![5, 30, 25]], device)?;
    // let cu_seqlens = grid_thw.i((.., 1))?.mul(&grid_thw.i((.., 2))?)?;
    // let grid_t = grid_thw.i((.., 0))?.to_vec1::<u32>()?;
    // println!("cu_seqlens: {}", cu_seqlens);
    // println!("cu_seqlens rank: {}", cu_seqlens.rank());
    // println!("grid_t: {:?}", grid_t);
    // let image_mask = Tensor::new(vec![0u32, 0, 0, 1, 0, 1], device)?;
    // let video_mask = Tensor::new(vec![0u32, 1, 0, 1, 0, 1], device)?;
    // let visual_mask = bitor_tensor(&image_mask, &video_mask)?;
    // println!("visual_mask: {}", visual_mask);
    // let x = Tensor::arange_step(0.0_f32, 5., 0.5, &device)?;
    // let x_int = x.to_dtype(candle_core::DType::U32)?;
    // println!("x: {}", x);
    // println!("x_int: {}", x_int);
    // let x_affine = x_int.affine(1.0, 1.0)?;
    // println!("x_affine: {}", x_affine);
    // let x_clamp = x_affine.clamp(0u32, 3u32)?;
    // println!("x_clamp: {}", x_clamp);
    // let wav_path = "./assets/audio/voice_01.wav";
    // let audio_tensor = load_audio_with_resample(wav_path, device, Some(16000))?;
    // println!("audio_tensor: {}", audio_tensor);
    // let string = "你好啊".to_string();
    // let vec_str: Vec<String>= string.chars().map(|c| c.to_string()).collect();
    // println!("vec_str: {:?}", vec_str);
    // let t = Tensor::rand(-1.0, 1.0, (2, 2), &device)?;
    // println!("t: {}", t);
    // let re_t = t.recip()?;
    // println!("re_t: {}", re_t);
    Ok(())
}