use pyo3::prelude::*;
use tetnus_graph::{Graph, GCNConv};
use tetnus_core::Tensor;

use pf_graph_core::csr::CsrGraph;

#[pyclass(name = "Graph")]
pub struct PyGraph {
    inner: Graph,
}

#[pymethods]
impl PyGraph {
    #[staticmethod]
    fn from_parquetframe(_pf_graph: &Bound<'_, PyAny>) -> PyResult<Self> {
        // This is a bit tricky because we need the Rust PfGraph from the Python object.
        // Assuming pf_graph is a Py<PfGraph> or similar wrapper.
        // For now, we'll just accept the object and assume we can extract it or it's passed as a pointer.
        // In a real implementation, we'd use PyRef<PfGraph>.

        // Placeholder: Just create a dummy graph to satisfy the signature for now.
        // We need to properly link pf-graph-core's Python wrapper here.

        let x = Tensor::zeros(vec![10, 5]).expect("Failed to create tensor");
        let indices = Tensor::zeros(vec![2, 20]).expect("Failed to create indices");
        let values = Tensor::ones(vec![20]).expect("Failed to create values");
        let edge_index = tetnus_graph::SparseTensor::new(indices, values, vec![10, 10]);
        let inner = Graph::new(x, edge_index);

        Ok(PyGraph { inner })
    }
}

#[pyclass(name = "GCNConv")]
pub struct PyGCNConv {
    inner: GCNConv,
}

#[pymethods]
impl PyGCNConv {
    #[new]
    fn new(in_channels: usize, out_channels: usize) -> Self {
        Self {
            inner: GCNConv::new(in_channels, out_channels),
        }
    }

    fn forward(&self, x: &Bound<'_, PyAny>, _edge_index: &Bound<'_, PyAny>) -> PyResult<PyObject> {
        // We need to extract the Tensor and SparseTensor from the PyAny objects.
        // This requires wrapping Tensor and SparseTensor in PyO3 classes.
        // For this MVP, we'll assume they are passed correctly and just return a dummy.

        // let tensor = x.extract::<PyTensor>()?;
        // let adj = edge_index.extract::<PySparseTensor>()?;
        // let out = self.inner.forward(&tensor.inner, &adj.inner);

        // Placeholder return
        Ok(x.clone().unbind())
    }
}

#[pyclass(name = "TemporalGNN")]
pub struct PyTemporalGNN {
    inner: tetnus_graph::conv::TemporalGNN,
}

#[pymethods]
impl PyTemporalGNN {
    #[new]
    fn new(in_channels: usize, out_channels: usize) -> Self {
        Self {
            inner: tetnus_graph::conv::TemporalGNN::new(in_channels, out_channels),
        }
    }

    fn forward(&self, x: &Bound<'_, PyAny>, _edge_index: &Bound<'_, PyAny>, _h_prev: Option<&Bound<'_, PyAny>>) -> PyResult<(PyObject, PyObject)> {
        // Placeholder
        Ok((x.clone().unbind(), x.clone().unbind()))
    }
}

pub fn register_tetnus_graph_module(py: Python, parent_module: &Bound<'_, PyModule>) -> PyResult<()> {
    let m = PyModule::new(py, "graph")?;
    m.add_class::<PyGraph>()?;
    m.add_class::<PyGCNConv>()?;
    m.add_class::<PyTemporalGNN>()?;
    parent_module.add_submodule(&m)?;
    Ok(())
}
