svod-tensor 0.1.0-alpha.3

High-level lazy tensor API for the Svod ML compiler
Documentation 
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use crate::*;
use svod_dtype::DType;

#[test]
fn test_where_basic() {
    // condition ? x : y
    let x = Tensor::from_slice([1.0f32, 2.0, 3.0, 4.0]);
    let y = Tensor::from_slice([10.0f32, 20.0, 30.0, 40.0]);
    let z = Tensor::from_slice([2.5f32, 2.5, 2.5, 2.5]); // Same shape for now

    // Create condition using comparison
    let condition = x.try_gt(&z).unwrap();

    // For now, just test that the method exists and compiles
    // Full integration tests will come later with proper execution
    let result = x.where_(&condition, &y);
    assert!(result.is_ok());
}

#[test]
fn test_maximum_shapes() {
    let a = Tensor::from_slice([1.0f32, 5.0, 3.0]);
    let b = Tensor::from_slice([2.0f32, 3.0, 4.0]);

    let result = a.maximum(&b);
    assert!(result.is_ok());

    // Check dtype is preserved
    assert_eq!(result.unwrap().uop().dtype(), DType::Float32);
}

#[test]
fn test_minimum_shapes() {
    let a = Tensor::from_slice([1.0f32, 5.0, 3.0]);
    let b = Tensor::from_slice([2.0f32, 3.0, 4.0]);

    let result = a.minimum(&b);
    assert!(result.is_ok());

    // Check dtype is preserved
    assert_eq!(result.unwrap().uop().dtype(), DType::Float32);
}

#[test]
fn test_clamp_both_bounds() {
    let x = Tensor::from_slice([-1.0f32, 0.0, 1.0, 2.0, 3.0]);
    // Same shape for now - broadcasting not yet implemented
    let min = Tensor::from_slice([0.0f32, 0.0, 0.0, 0.0, 0.0]);
    let max = Tensor::from_slice([2.0f32, 2.0, 2.0, 2.0, 2.0]);

    let result = x.clamp().min(&min).max(&max).call();
    assert!(result.is_ok());
}

#[test]
fn test_clamp_only_min() {
    let x = Tensor::from_slice([-1.0f32, 0.0, 1.0, 2.0, 3.0]);
    // Same shape for now - broadcasting not yet implemented
    let min = Tensor::from_slice([0.0f32, 0.0, 0.0, 0.0, 0.0]);

    let result = x.clamp().min(&min).call();
    assert!(result.is_ok());
}

#[test]
fn test_clamp_only_max() {
    let x = Tensor::from_slice([-1.0f32, 0.0, 1.0, 2.0, 3.0]);
    // Same shape for now - broadcasting not yet implemented
    let max = Tensor::from_slice([2.0f32, 2.0, 2.0, 2.0, 2.0]);

    let result = x.clamp().max(&max).call();
    assert!(result.is_ok());
}

#[test]
fn test_clip_alias() {
    let x = Tensor::from_slice([-1.0f32, 0.0, 1.0, 2.0, 3.0]);
    // Same shape for now - broadcasting not yet implemented
    let min = Tensor::from_slice([0.0f32, 0.0, 0.0, 0.0, 0.0]);
    let max = Tensor::from_slice([2.0f32, 2.0, 2.0, 2.0, 2.0]);

    let result = x.clip().min(&min).max(&max).call();
    assert!(result.is_ok());
}