//! Simple CUDA functionality test to verify compatibility
//!
//! This test verifies that the updated candle-core with CUDA support
//! can successfully create tensors and perform basic operations.
#![allow(unused_crate_dependencies)]

use candle_core::{Device, Tensor};
use candle_nn::{linear, Module, VarBuilder, VarMap};

/// Format a tensor's dimensions as a display string without using Debug formatting.
fn fmt_dims(dims: &[usize]) -> String {
    let parts: Vec<String> = dims.iter().map(|d| d.to_string()).collect();
    format!("[{}]", parts.join(", "))
}

/// Test basic CUDA tensor operations
pub fn test_cuda_basic() -> Result<(), Box<dyn std::error::Error>> {
    println!("Testing CUDA compatibility...");

    // Try to get CUDA device
    match Device::new_cuda(0) {
        Ok(device) => {
            println!("[OK] CUDA device 0 available");

            // Create a simple tensor
            let tensor = Tensor::randn(0_f32, 1.0, (4, 4), &device)?;
            println!("[OK] Created CUDA tensor: {}", fmt_dims(tensor.dims()));

            // Perform basic operations
            let result = tensor.matmul(&tensor.t()?)?;
            println!(
                "[OK] Matrix multiplication successful: {}",
                fmt_dims(result.dims())
            );

            Ok(())
        },
        Err(e) => {
            println!("[WARN] CUDA device not available: {}", e);
            println!("This is expected if no GPU is present, but CUDA compilation succeeded");
            Ok(())
        },
    }
}

/// Test candle-nn components with CUDA
pub fn test_cuda_neural_network() -> Result<(), Box<dyn std::error::Error>> {
    println!("Testing CUDA neural network components...");

    if let Ok(device) = Device::new_cuda(0) {
        let varmap = VarMap::new();
        let vs = VarBuilder::from_varmap(&varmap, candle_core::DType::F32, &device);

        // Create a simple linear layer
        let linear_layer = linear(10, 5, vs.pp("linear"))?;
        let input = Tensor::randn(0_f32, 1.0, (1, 10), &device)?;

        let output = linear_layer.forward(&input)?;
        println!(
            "[OK] Neural network forward pass successful: {}",
            fmt_dims(output.dims())
        );
    } else {
        println!("[WARN] CUDA neural network test skipped (no GPU)");
    }

    Ok(())
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn verify_cuda_compilation() {
        // This test just verifies that CUDA code compiles
        // The actual runtime test is optional since CI may not have GPU
        println!("CUDA compilation test passed!");

        // Try to run basic test but don't fail if no GPU
        let _ = test_cuda_basic();
        let _ = test_cuda_neural_network();
    }
}

fn main() -> Result<(), Box<dyn std::error::Error>> {
    test_cuda_basic()?;
    test_cuda_neural_network()?;
    println!("[DONE] CUDA compatibility verification complete!");
    Ok(())
}