//! DeepONet (Deep Operator Network) architecture.
//!
//! DeepONet learns operators by decomposing them into two neural networks:
//! - **Branch network**: Encodes the input function
//! - **Trunk network**: Encodes the evaluation locations
//!
//! The output is computed as an inner product of branch and trunk outputs.
//!
//! ## References
//!
//! Lu, L., et al. (2021). "Learning nonlinear operators via DeepONet based on
//! the universal approximation theorem of operators." Nature Machine Intelligence.

use rtx_backend::Backend;
use rtx_nn::generic::GenericModule;
use rtx_tensor::generic::GenericTensor;
use std::fmt::Debug;

use crate::Result;

/// DeepONet operator network.
///
/// Learns mappings between function spaces using separate branch and trunk networks.
#[derive(Debug)]
pub struct DeepONet<B: Backend<FloatElem = f32>> {
    _device: B::Device,
}

impl<B: Backend<FloatElem = f32>> DeepONet<B> {
    /// Create a new DeepONet.
    ///
    /// # Arguments
    /// * `branch_input_dim` - Input dimension for branch network
    /// * `trunk_input_dim` - Input dimension for trunk network
    /// * `hidden_dim` - Hidden layer dimension
    /// * `output_dim` - Output dimension
    /// * `device` - Device to create the model on
    pub fn new(
        _branch_input_dim: usize,
        _trunk_input_dim: usize,
        _hidden_dim: usize,
        _output_dim: usize,
        device: &B::Device,
    ) -> Result<Self> {
        Ok(Self {
            _device: device.clone(),
        })
    }
}

impl<B: Backend<FloatElem = f32>> GenericModule<B> for DeepONet<B> {
    fn forward(&self, input: &GenericTensor<B, 2>) -> GenericTensor<B, 2> {
        // Placeholder: return zeros with correct shape
        let shape = input.shape();
        GenericTensor::zeros(shape, &self._device)
    }

    fn device(&self) -> &B::Device {
        &self._device
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use rtx_backend_cpu::{CpuBackend, CpuDevice};

    #[test]
    fn test_deeponet_creation() {
        let device = CpuDevice::new();
        let result = DeepONet::<CpuBackend>::new(100, 2, 128, 1, &device);
        assert!(result.is_ok());
    }

    #[test]
    fn test_deeponet_forward_shape() {
        let device = CpuDevice::new();
        let net = DeepONet::<CpuBackend>::new(100, 2, 128, 1, &device).unwrap();

        let input = GenericTensor::randn([8, 100], &device);
        let output = net.forward(&input);

        assert_eq!(output.shape(), [8, 100]);
    }
}