use std::time::Instant;

use aha::{models::{qwen2_5vl::generate::Qwen2_5VLGenerateModel, GenerateModel}, ModelType};
use anyhow::{Result};
use candle_core::{DType, Device};
use openai_dive::v1::resources::chat::ChatCompletionParameters;


#[test]
fn qwen2_5vl_generate() -> Result<()> {
    // test with cpu :(太慢了, : RUST_BACKTRACE=1 cargo test qwen2_5vl_generate -- --nocapture
    // test with cuda: RUST_BACKTRACE=1 cargo test -F cuda qwen2_5vl_generate -- --nocapture
    // test with cuda+flash-attn: RUST_BACKTRACE=1 cargo test -F cuda,flash-attn qwen2_5vl_generate -- --nocapture
    let device = Device::cuda_if_available(0)?;
    let dtype = DType::BF16;

    let model_path = "/home/jhq/huggingface_model/Qwen/Qwen2.5-VL-3B-Instruct/";
    
    let message = r#"
    {
        "model": "qwen2.5vl",
        "messages": [
            {
                "role": "user",
                "content": [ 
                    {
                        "type": "image",
                        "image_url": 
                        {
                            "url": "file://./assets/img/ocr_test.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 = Qwen2_5VLGenerateModel::init(model_path, &device, dtype)?;
    let mut model = ModelType::init(ModelType::Qwen2_5VL, 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)?;
    println!("generate: \n{}", result);    
    let i_duration = i_start.elapsed();
    println!("Time elapsed in generate is: {:?}", i_duration);

    Ok(())
}