use std::time::Instant;

use aha::{chat::ChatCompletionParameters, models::lfm2vl::generate::Lfm2VLGenerateModel};
use anyhow::Result;
#[test]
fn lfm2vl_generate() -> Result<()> {
    // test with cuda: RUST_BACKTRACE=1 cargo test -F cuda --test test_lfm2vl lfm2vl_generate -r -- --nocapture
    
    let save_dir =
        aha::utils::get_default_save_dir().ok_or(anyhow::anyhow!("Failed to get save dir"))?;
    let model_path = format!("{}/LiquidAI/LFM2.5-VL-1.6B/", save_dir);
    let message = r#"
    {
        "model": "lfm2vl",
        "messages": [
            {
                "role": "user",
                "content": [ 
                    {
                        "type": "image",
                        "image_url": 
                        {
                            "url": "file://./assets/img/ocr_test1.png"
                        }
                    },             
                    {
                        "type": "text", 
                        "text": "请分析图片并提取所有可见文本内容，按从左到右、从上到下的布局，返回纯文本"
                    }
                ]
            }
        ]
    }
    "#;
    let mes: ChatCompletionParameters = serde_json::from_str(message)?;
    let i_start = Instant::now();
    let mut model = Lfm2VLGenerateModel::init(&model_path, None, None)?;
    let i_duration = i_start.elapsed();
    println!("Time elapsed in load model is: {:?}", i_duration);

    let i_start = Instant::now();
    let result = model.generate(mes)?;
    let i_duration = i_start.elapsed();
    // println!("generate: \n {:?}", result);
    // if let Some(usage) = &result.usage {
    //     let num_token = usage.total_tokens;
    //     let duration_secs = i_duration.as_secs_f64();
    //     let tps = num_token as f64 / duration_secs;
    //     println!("Tokens per second (TPS): {:.2}", tps);
    // }
    // println!("Time elapsed in generate is: {:?}", i_duration);

    Ok(())
}