//! BLAS Backend Implementation (Stub)
//!
//! This is a stub implementation that would use BLAS libraries (OpenBLAS, Intel MKL, etc.)
//! for maximum performance on linear algebra operations when available.

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

/// BLAS backend stub that would provide high-performance linear algebra operations
///
/// This is currently a stub implementation. In a full implementation, this would
/// link against BLAS/LAPACK libraries for optimal performance.
#[derive(Debug)]
pub struct BlasBackend {
    is_available: bool,
    blas_type: BlasType,
}

#[derive(Debug, Clone)]
pub enum BlasType {
    OpenBLAS,
    IntelMKL,
    Atlas,
    GenericBLAS,
    Unknown,
}

impl BlasBackend {
    /// Create a new BLAS backend
    pub fn new() -> Self {
        let (is_available, blas_type) = Self::detect_blas_library();

        Self {
            is_available,
            blas_type,
        }
    }

    /// Detect which BLAS library is available
    fn detect_blas_library() -> (bool, BlasType) {
        // In a full implementation, this would:
        // 1. Try to link against different BLAS libraries
        // 2. Test basic BLAS function calls
        // 3. Identify specific implementation (MKL, OpenBLAS, etc.)

        // For now, return not available
        (false, BlasType::Unknown)
    }

    /// Get performance characteristics based on BLAS type
    fn get_performance_for_blas_type(&self, op: OperationType) -> BackendPerformance {
        let (base_throughput, base_bandwidth) = match self.blas_type {
            BlasType::IntelMKL => (100e6, 80.0),   // Highest performance
            BlasType::OpenBLAS => (80e6, 60.0),    // Very good performance
            BlasType::Atlas => (50e6, 40.0),       // Good performance
            BlasType::GenericBLAS => (30e6, 25.0), // Acceptable performance
            BlasType::Unknown => (20e6, 20.0),     // Conservative estimate
        };

        let (throughput_multiplier, overhead) = match op {
            OperationType::GEMM => (1.0, 5.0), // BLAS excels at GEMM
            OperationType::GEMV => (0.8, 3.0), // Very good for GEMV
            OperationType::ElementwiseAdd => (0.3, 10.0), // Not ideal for element-wise
            OperationType::ElementwiseMul => (0.3, 10.0), // Not ideal for element-wise
            OperationType::SVD => (0.6, 50.0), // Good for decompositions
            OperationType::QR => (0.7, 30.0),  // Very good for QR
            OperationType::Cholesky => (0.8, 20.0), // Excellent for Cholesky
            _ => (0.5, 15.0),                  // Moderate for others
        };

        BackendPerformance {
            throughput_ops_per_sec: base_throughput * throughput_multiplier,
            memory_bandwidth_gb_per_sec: base_bandwidth,
            setup_overhead_us: overhead,
            optimal_size_range: (128, 50000), // BLAS is great for large matrices
        }
    }
}

impl ComputeBackend for BlasBackend {
    fn name(&self) -> &str {
        match self.blas_type {
            BlasType::OpenBLAS => "OpenBLAS (stub)",
            BlasType::IntelMKL => "Intel MKL (stub)",
            BlasType::Atlas => "ATLAS BLAS (stub)",
            BlasType::GenericBLAS => "Generic BLAS (stub)",
            BlasType::Unknown => "Unknown BLAS (stub)",
        }
    }

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

    fn supports_dtype(&self, dtype: DType) -> bool {
        // BLAS has excellent support for f32/f64, limited support for integers
        matches!(dtype, DType::Float64 | DType::Float32)
    }

    fn supports_operation(&self, op: OperationType) -> bool {
        matches!(
            op,
            OperationType::GEMM | OperationType::GEMV |
            OperationType::ElementwiseAdd | // Via AXPY
            OperationType::SVD | OperationType::QR | OperationType::Cholesky
        )
    }

    fn optimal_threshold(&self, op: OperationType) -> usize {
        match op {
            OperationType::GEMM => 256, // BLAS really shines on large matrices
            OperationType::GEMV => 128, // Good threshold for GEMV
            OperationType::SVD | OperationType::QR | OperationType::Cholesky => 64, // Good for decompositions
            _ => 200,
        }
    }

    fn performance_characteristics(&self, op: OperationType) -> BackendPerformance {
        self.get_performance_for_blas_type(op)
    }

    // 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(
            "BLAS 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(
            "BLAS 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(
            "BLAS 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(
            "BLAS 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(
            "BLAS 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(
            "BLAS 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(
            "BLAS backend not implemented (stub)".to_string(),
        ))
    }
}

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

    #[test]
    fn test_blas_stub() {
        let backend = BlasBackend::new();
        // Stub implementation is not available
        assert!(!backend.is_available());
        println!("BLAS backend name: {}", backend.name());
    }

    #[test]
    fn test_performance_characteristics() {
        let backend = BlasBackend::new();
        let perf = backend.performance_characteristics(OperationType::GEMM);

        assert!(perf.throughput_ops_per_sec > 0.0);
        assert!(perf.optimal_size_range.1 > perf.optimal_size_range.0);
    }
}
