//! NumPy Backend Implementation (Stub)
//!
//! This is a stub implementation that can be extended when PyO3 and NumPy
//! dependencies are available. For now, it provides the interface structure.

use crate::storage::advanced_matrix::{
    backend::{BackendError, BackendPerformance, BackendResult, ComputeBackend, OperationType},
    numeric_type::DType,
};

/// NumPy backend stub that would leverage Python's scientific computing ecosystem
///
/// This is currently a stub implementation. In a full implementation, this would
/// use PyO3 to call NumPy and SciPy functions for optimal performance.
#[derive(Debug)]
pub struct NumPyBackend {
    is_available: bool,
}

impl NumPyBackend {
    /// Create a new NumPy backend
    pub fn new() -> Self {
        Self {
            // For now, NumPy backend is not available (would check for PyO3 + NumPy)
            is_available: false,
        }
    }
}

impl ComputeBackend for NumPyBackend {
    fn name(&self) -> &str {
        "NumPy (stub)"
    }

    fn is_available(&self) -> bool {
        self.is_available
    }

    fn supports_dtype(&self, dtype: DType) -> bool {
        // NumPy would support these types
        matches!(
            dtype,
            DType::Float64 | DType::Float32 | DType::Int64 | DType::Int32 | DType::Bool
        )
    }

    fn supports_operation(&self, op: OperationType) -> bool {
        // NumPy would support these operations
        matches!(
            op,
            OperationType::GEMM
                | OperationType::GEMV
                | OperationType::ElementwiseAdd
                | OperationType::ElementwiseMul
                | OperationType::Sum
                | OperationType::Max
                | OperationType::Min
                | OperationType::SVD
                | OperationType::QR
                | OperationType::Cholesky
        )
    }

    fn optimal_threshold(&self, op: OperationType) -> usize {
        match op {
            OperationType::GEMM => 64,
            OperationType::ElementwiseAdd | OperationType::ElementwiseMul => 1000,
            OperationType::SVD | OperationType::QR | OperationType::Cholesky => 32,
            _ => 100,
        }
    }

    fn performance_characteristics(&self, op: OperationType) -> BackendPerformance {
        // Theoretical NumPy performance characteristics
        match op {
            OperationType::GEMM => BackendPerformance {
                throughput_ops_per_sec: 50e6,
                memory_bandwidth_gb_per_sec: 25.0,
                setup_overhead_us: 50.0,
                optimal_size_range: (64, 10000),
            },
            OperationType::SVD | OperationType::QR | OperationType::Cholesky => {
                BackendPerformance {
                    throughput_ops_per_sec: 10e6,
                    memory_bandwidth_gb_per_sec: 20.0,
                    setup_overhead_us: 100.0,
                    optimal_size_range: (32, 5000),
                }
            }
            _ => BackendPerformance {
                throughput_ops_per_sec: 25e6,
                memory_bandwidth_gb_per_sec: 15.0,
                setup_overhead_us: 25.0,
                optimal_size_range: (100, 5000),
            },
        }
    }

    // All operations return "not available" since this is a stub
    unsafe fn gemm_raw(
        &self,
        _dtype: DType,
        _a: *const u8,
        _a_shape: (usize, usize),
        _b: *const u8,
        _b_shape: (usize, usize),
        _c: *mut u8,
        _c_shape: (usize, usize),
        _alpha: *const u8,
        _beta: *const u8,
    ) -> BackendResult<()> {
        Err(BackendError::BackendUnavailable(
            "NumPy backend not implemented (stub)".to_string(),
        ))
    }

    unsafe fn gemv_raw(
        &self,
        _dtype: DType,
        _matrix: *const u8,
        _matrix_shape: (usize, usize),
        _vector: *const u8,
        _result: *mut u8,
        _alpha: *const u8,
        _beta: *const u8,
    ) -> BackendResult<()> {
        Err(BackendError::BackendUnavailable(
            "NumPy backend not implemented (stub)".to_string(),
        ))
    }

    unsafe fn elementwise_add_raw(
        &self,
        _dtype: DType,
        _a: *const u8,
        _b: *const u8,
        _result: *mut u8,
        _len: usize,
    ) -> BackendResult<()> {
        Err(BackendError::BackendUnavailable(
            "NumPy backend not implemented (stub)".to_string(),
        ))
    }

    unsafe fn elementwise_mul_raw(
        &self,
        _dtype: DType,
        _a: *const u8,
        _b: *const u8,
        _result: *mut u8,
        _len: usize,
    ) -> BackendResult<()> {
        Err(BackendError::BackendUnavailable(
            "NumPy backend not implemented (stub)".to_string(),
        ))
    }

    unsafe fn reduce_sum_raw(
        &self,
        _dtype: DType,
        _data: *const u8,
        _len: usize,
        _result: *mut u8,
    ) -> BackendResult<()> {
        Err(BackendError::BackendUnavailable(
            "NumPy backend not implemented (stub)".to_string(),
        ))
    }

    unsafe fn reduce_max_raw(
        &self,
        _dtype: DType,
        _data: *const u8,
        _len: usize,
        _result: *mut u8,
    ) -> BackendResult<()> {
        Err(BackendError::BackendUnavailable(
            "NumPy backend not implemented (stub)".to_string(),
        ))
    }

    unsafe fn reduce_min_raw(
        &self,
        _dtype: DType,
        _data: *const u8,
        _len: usize,
        _result: *mut u8,
    ) -> BackendResult<()> {
        Err(BackendError::BackendUnavailable(
            "NumPy backend not implemented (stub)".to_string(),
        ))
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_numpy_stub() {
        let backend = NumPyBackend::new();
        // Stub implementation is not available
        assert!(!backend.is_available());
        assert_eq!(backend.name(), "NumPy (stub)");
    }
}
