tensor_interop.rs

use numpy::{IntoPyArray, PyArray1}; // Importing NumPy for array operations
use pyo3::prelude::*; // Importing PyO3 for Python interoperability
use pyo3::{PyResult, Python}; // Importing necessary PyO3 components
use tch::{Kind, Tensor}; // Importing Torch for tensor operations

// Function to convert a PyTorch tensor to a NumPy array
fn torch_to_numpy<T: Clone>(py: Python, tensor: &Tensor) -> PyResult<PyArray1<T>> {
    // Extract data from the tensor as a slice
    let data = tensor.data();
    let slice = unsafe { std::slice::from_raw_parts(data.as_ptr() as *const T, data.len()) };

    // Create a NumPy array from the slice
    let numpy_array = PyArray1::from_slice(py, slice);
    Ok(numpy_array)
    
    // Possible error: Unsafe pointer dereferencing
    // Solution: Ensure the tensor data is valid and correctly handled to prevent undefined behavior.
}

// Function to convert a NumPy array to a PyTorch tensor
fn numpy_to_torch<T: Clone + 'static>(array: &PyArray1<T>) -> Tensor {
    // Convert the NumPy array to a slice
    let data = array.as_slice().unwrap();
    // Create a PyTorch tensor from the slice
    let tensor = Tensor::of_slice(data).view([-1]);
    tensor

    // Possible error: Array conversion failure
    // Solution: Validate the NumPy array and handle unwrap safely to avoid panics.
}

// Function to generate audio samples using a PyTorch tensor
fn generate_audio(tensor: &Tensor, sample_rate: i64) -> Vec<f32> {
    // Assuming the tensor represents audio samples
    let num_samples = tensor.size()[0];
    let mut audio_samples = vec![0.0; num_samples as usize];

    // Iterate over the tensor elements and populate the audio samples
    for (i, &sample) in tensor.data().as_slice::<f32>().unwrap().iter().enumerate() {
        audio_samples[i] = sample;
    }

    audio_samples

    // Possible error: Tensor to slice conversion failure
    // Solution: Ensure the tensor contains valid audio data and handle unwrap safely to avoid panics.
}

// Python module to expose tensor interoperability functions
#[pymodule]
fn tensor_interop(_py: Python, m: &PyModule) -> PyResult<()> {
    // Function to convert a PyTorch tensor to a NumPy array
    #[pyfn(m)]
    #[pyo3(name = "torch_to_numpy")]
    fn torch_to_numpy_py<'py>(py: Python<'py>, tensor: &PyAny) -> PyResult<&'py PyArray1<f32>> {
        // Extract the PyTorch tensor from the PyAny object
        let tensor = tensor.extract::<Tensor>()?;
        // Convert the PyTorch tensor to a NumPy array
        let numpy_array = torch_to_numpy(py, &tensor)?;
        Ok(numpy_array)

        // Possible error: Tensor extraction failure
        // Solution: Ensure the PyAny object contains a valid PyTorch tensor and handle extraction safely.
    }

    // Function to convert a NumPy array to a PyTorch tensor
    #[pyfn(m)]
    #[pyo3(name = "numpy_to_torch")]
    fn numpy_to_torch_py(array: &PyArray1<f32>) -> PyResult<Tensor> {
        // Convert the NumPy array to a PyTorch tensor
        let tensor = numpy_to_torch(array);
        Ok(tensor)

        // Possible error: Array conversion failure
        // Solution: Ensure the NumPy array contains valid data and handle conversion safely.
    }

    // Function to generate audio samples from a PyTorch tensor
    #[pyfn(m)]
    #[pyo3(name = "generate_audio")]
    fn generate_audio_py(tensor: &PyAny, sample_rate: i64) -> PyResult<Vec<f32>> {
        // Extract the PyTorch tensor from the PyAny object
        let tensor = tensor.extract::<Tensor>()?;
        // Generate audio samples using the PyTorch tensor
        let audio_samples = generate_audio(&tensor, sample_rate);
        Ok(audio_samples)

        // Possible error: Tensor extraction failure
        // Solution: Ensure the PyAny object contains a valid PyTorch tensor and handle extraction safely.
    }

    Ok(())
}