//! CUDA kernel wrappers for connected-component labeling.
//!
//! Uses the label-equivalence algorithm (Oliveira et al., 2010) which is
//! well-suited for GPU parallelism:
//!
//! 1. **Init**: Each pixel gets its own label (label[i] = i)
//! 2. **Merge**: Parallel passes where each pixel checks neighbors and
//!    propagates the minimum label via atomic operations
//! 3. **Flatten**: Resolve equivalence chains to canonical labels
//!
//! The kernel source (PTX) is embedded at compile time when building with
//! the `cuda` feature.

use crate::connectivity::Connectivity;
use crate::label::LabelResult;

/// Handle to a CUDA device.
///
/// Wraps `cudarc::driver::CudaDevice` when the feature is enabled.
pub struct CudaDevice {
    // When cudarc is integrated:
    // inner: std::sync::Arc<cudarc::driver::CudaDevice>,
    _private: (),
}

impl CudaDevice {
    /// Initialize CUDA device by ordinal.
    pub fn new(_ordinal: usize) -> Result<Self, String> {
        // TODO: Initialize cudarc device
        // let inner = cudarc::driver::CudaDevice::new(ordinal)
        //     .map_err(|e| format!("CUDA init failed: {e}"))?;
        Err("CUDA support not yet compiled — install with `cuda` feature".to_string())
    }
}

/// Result of GPU-based connected-component labeling.
pub struct GpuLabelResult {
    pub labels: Vec<u32>,
    pub width: usize,
    pub height: usize,
}

impl From<GpuLabelResult> for LabelResult {
    fn from(gpu: GpuLabelResult) -> Self {
        LabelResult {
            labels: gpu.labels,
            width: gpu.width,
            height: gpu.height,
        }
    }
}

/// Run connected-component labeling on GPU, return label grid on CPU.
///
/// # Algorithm (label-equivalence, 3 kernel launches)
///
/// ```text
/// Kernel 1 — init_labels:
///   label[i] = (mask[i]) ? i : UINT32_MAX
///
/// Kernel 2 — merge (iterated until convergence):
///   for each pixel i with valid label:
///     for each neighbor j (4 or 8 connected):
///       if value[i] == value[j] && label[i] != label[j]:
///         atomicMin(&label[min(i,j)], label[max(i,j)])
///
/// Kernel 3 — flatten:
///   while label[i] != label[label[i]]:
///     label[i] = label[label[i]]
/// ```
///
/// After flatten, D2H transfer of only the u32 label grid (4 bytes/pixel
/// vs 4 bytes for f32 or 8 bytes for f64 source data).
pub fn gpu_label_regions(
    _device: &CudaDevice,
    _gpu_ptr: u64,
    width: usize,
    height: usize,
    _mask_gpu_ptr: Option<u64>,
    _connectivity: Connectivity,
) -> GpuLabelResult {
    // Placeholder — actual implementation requires cudarc + PTX kernels
    //
    // Real implementation would:
    // 1. Allocate device buffer for labels: device.alloc_zeros(width * height)
    // 2. Launch init_labels kernel
    // 3. Launch merge kernel in a loop until no changes
    // 4. Launch flatten kernel
    // 5. Copy label buffer to host: device.dtoh_sync_copy(&labels_dev)
    //
    // The PTX kernel source would be compiled from CUDA C at build time
    // using cc::Build or embedded as a static string.

    GpuLabelResult {
        labels: vec![0u32; width * height],
        width,
        height,
    }
}

// The CUDA kernel source (to be compiled to PTX at build time):
//
// ```cuda
// extern "C" __global__ void init_labels(
//     const float* __restrict__ values,
//     const bool* __restrict__ mask,  // nullable
//     uint32_t* __restrict__ labels,
//     int width, int height
// ) {
//     int idx = blockIdx.x * blockDim.x + threadIdx.x;
//     if (idx >= width * height) return;
//     labels[idx] = (mask == nullptr || mask[idx]) ? idx : UINT32_MAX;
// }
//
// extern "C" __global__ void merge_labels(
//     const float* __restrict__ values,
//     uint32_t* __restrict__ labels,
//     int width, int height,
//     int connectivity,  // 4 or 8
//     int* __restrict__ changed
// ) {
//     int idx = blockIdx.x * blockDim.x + threadIdx.x;
//     if (idx >= width * height) return;
//     if (labels[idx] == UINT32_MAX) return;
//
//     int row = idx / width;
//     int col = idx % width;
//     float val = values[idx];
//
//     // Check 4-connected neighbors (+ diagonals if connectivity == 8)
//     int dx[] = {-1, 1, 0, 0, -1, -1, 1, 1};
//     int dy[] = {0, 0, -1, 1, -1, 1, -1, 1};
//     int num_neighbors = (connectivity == 8) ? 8 : 4;
//
//     for (int d = 0; d < num_neighbors; d++) {
//         int nc = col + dx[d];
//         int nr = row + dy[d];
//         if (nc < 0 || nc >= width || nr < 0 || nr >= height) continue;
//         int nidx = nr * width + nc;
//         if (labels[nidx] == UINT32_MAX) continue;
//         if (values[nidx] != val) continue;
//
//         uint32_t label_a = labels[idx];
//         uint32_t label_b = labels[nidx];
//         if (label_a != label_b) {
//             uint32_t min_label = min(label_a, label_b);
//             uint32_t max_label = max(label_a, label_b);
//             atomicMin(&labels[max_label], min_label);
//             *changed = 1;
//         }
//     }
// }
//
// extern "C" __global__ void flatten_labels(
//     uint32_t* __restrict__ labels,
//     int n
// ) {
//     int idx = blockIdx.x * blockDim.x + threadIdx.x;
//     if (idx >= n) return;
//     if (labels[idx] == UINT32_MAX) return;
//     while (labels[idx] != labels[labels[idx]]) {
//         labels[idx] = labels[labels[idx]];
//     }
// }
// ```
