//! Integration test to verify that rtx-science can use core RTX types

use crate::*;

#[test]
fn test_import_core_types() {
    // Test that we can use the imported types without compilation errors

    // Test Tensor and Device (from rtx-tensor)
    let device = Device::cpu();
    let _tensor = Tensor::zeros(&[2, 3], &device).unwrap();

    // Test Variable (from rtx-autograd)
    let tensor = Tensor::ones(&[2, 2], &device).unwrap();
    let var = Variable::new(tensor, true);
    assert!(var.requires_grad());

    // Test error types
    let _error: AutogradError = AutogradError::BackwardError("test".to_string());
    let _tensor_error: TensorError = TensorError::Shape {
        message: "test".to_string(),
    };

    // Note: DistributedContext is temporarily disabled due to NCCL compilation issues
    // When rtx-distributed is re-enabled, add the following tests:
    // let ctx = DistributedContext::uninitialized();
    // assert!(ctx.is_master());
    // assert_eq!(ctx.world_size(), 1);
    // assert_eq!(ctx.rank(), 0);
}

#[test]
fn test_scientific_tensor() {
    // Test our ScientificTensor still works
    let tensor = ScientificTensor::zeros(&[3, 3]);
    assert_eq!(tensor.shape(), &[3, 3]);

    let tensor2 = ScientificTensor::ones(&[3, 3]);
    let result = tensor.add(&tensor2).unwrap();
    assert_eq!(result.data().sum(), 9.0);
}