add Qwen3VL model

tests/messy_test.rs

@@ -1,13 +1,25 @@
-use aha::utils::audio_utils::load_audio_with_resample;
+use aha::utils::{tensor_utils::bitor_tensor};
 use anyhow::Result;
+use candle_core::Tensor;
 
 #[test]
 fn messy_test() -> Result<()> {
-    let device = candle_core::Device::Cpu;
-    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 device = &candle_core::Device::Cpu;
+    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);