//! Comprehensive scirs2-linalg integration for advanced linear algebra operations
//!
//! This module provides the structure for integration with scirs2-linalg's high-performance
//! linear algebra algorithms while maintaining PyTorch compatibility through the torsh-tensor API.
//!
//! # Features
//!
//! - **Advanced Decompositions**: LU, QR, SVD, Cholesky with optimized algorithms
//! - **Eigenvalue Problems**: Standard and generalized eigenvalue decompositions
//! - **Linear Solvers**: Direct and iterative solvers for linear systems
//! - **Matrix Functions**: Exponential, logarithm, square root, trigonometric
//! - **Numerical Stability**: Condition number estimation, matrix scaling
//! - **Hardware Acceleration**: SIMD and GPU-accelerated routines

use crate::TorshResult;
use std::collections::HashMap;
use torsh_core::TorshError;
use torsh_tensor::Tensor;

// SciRS2 imports (prepared for when APIs stabilize)
#[cfg(feature = "scirs2-integration")]
use scirs2_core as _; // Import for future use
#[cfg(feature = "scirs2-integration")]
use scirs2_linalg as _; // Import for future use

/// Advanced linear algebra processor using scirs2-linalg capabilities
pub struct SciRS2LinalgProcessor {
    config: LinalgConfig,
    #[allow(dead_code)]
    solver_cache: HashMap<String, String>, // Simplified cache
    decomposition_cache: HashMap<String, DecompositionResult>,
}

/// Configuration for linear algebra operations
#[derive(Debug, Clone)]
pub struct LinalgConfig {
    /// Numerical tolerance for computations
    pub tolerance: f64,
    /// Maximum iterations for iterative methods
    pub max_iterations: usize,
    /// Enable caching of decompositions
    pub enable_caching: bool,
    /// Use GPU acceleration when available
    pub use_gpu: bool,
    /// SIMD optimization level
    pub simd_level: SIMDLevel,
    /// Pivot strategy for decompositions
    pub pivot_strategy: PivotStrategy,
}

#[derive(Debug, Clone)]
pub enum SIMDLevel {
    None,
    Basic,
    Advanced,
    Maximum,
}

#[derive(Debug, Clone)]
pub enum PivotStrategy {
    None,
    Partial,
    Complete,
    Rook,
}

/// Result container for matrix decompositions
#[derive(Debug, Clone)]
pub enum DecompositionResult {
    Lu {
        p: Tensor,
        l: Tensor,
        u: Tensor,
    },
    Qr {
        q: Tensor,
        r: Tensor,
    },
    Svd {
        u: Tensor,
        s: Tensor,
        vt: Tensor,
    },
    Cholesky {
        l: Tensor,
    },
    Eigen {
        values: Tensor,
        vectors: Option<Tensor>,
    },
    Schur {
        q: Tensor,
        t: Tensor,
    },
}

/// Matrix norm types
#[derive(Debug, Clone, Copy)]
pub enum MatrixNormType {
    Frobenius,
    OneNorm,
    InfNorm,
    TwoNorm,
    Nuclear,
}

impl Default for LinalgConfig {
    fn default() -> Self {
        Self {
            tolerance: 1e-12,
            max_iterations: 1000,
            enable_caching: true,
            use_gpu: false,
            simd_level: SIMDLevel::Advanced,
            pivot_strategy: PivotStrategy::Partial,
        }
    }
}

impl SciRS2LinalgProcessor {
    pub fn new(config: LinalgConfig) -> Self {
        Self {
            config,
            solver_cache: HashMap::new(),
            decomposition_cache: HashMap::new(),
        }
    }

    /// Create with default configuration
    pub fn with_default_config() -> Self {
        Self::new(LinalgConfig::default())
    }

    /// Configure processor for high-precision computations
    pub fn high_precision() -> Self {
        Self::new(LinalgConfig {
            tolerance: 1e-15,
            max_iterations: 5000,
            enable_caching: true,
            use_gpu: false,
            simd_level: SIMDLevel::Maximum,
            pivot_strategy: PivotStrategy::Complete,
        })
    }

    /// Configure processor for GPU acceleration
    pub fn gpu_accelerated() -> Self {
        Self::new(LinalgConfig {
            tolerance: 1e-10,
            max_iterations: 1000,
            enable_caching: true,
            use_gpu: true,
            simd_level: SIMDLevel::Maximum,
            pivot_strategy: PivotStrategy::Partial,
        })
    }

    /// Enhanced LU decomposition with pivoting and conditioning
    pub fn lu_decomposition(&mut self, matrix: &Tensor) -> TorshResult<DecompositionResult> {
        self.validate_square_matrix(matrix, "LU decomposition")?;

        let shape = matrix.shape();
        let shape_dims = shape.dims();
        let cache_key = format!("lu_{}x{}", shape_dims[0], shape_dims[1]);
        if self.config.enable_caching {
            if let Some(cached) = self.decomposition_cache.get(&cache_key) {
                return Ok(cached.clone());
            }
        }

        // For now, delegate to existing torsh-linalg implementation
        let (p, l, u) = crate::decomposition::lu(matrix)?;
        let result = DecompositionResult::Lu { p, l, u };

        if self.config.enable_caching {
            self.decomposition_cache.insert(cache_key, result.clone());
        }

        Ok(result)
    }

    /// Enhanced QR decomposition with column pivoting
    pub fn qr_decomposition(&mut self, matrix: &Tensor) -> TorshResult<DecompositionResult> {
        // For now, delegate to existing torsh-linalg implementation
        let (q, r) = crate::decomposition::qr(matrix)?;
        Ok(DecompositionResult::Qr { q, r })
    }

    /// Enhanced SVD with advanced algorithms
    pub fn svd_decomposition(&mut self, matrix: &Tensor) -> TorshResult<DecompositionResult> {
        // For now, delegate to existing torsh-linalg implementation
        let (u, s, vt) = crate::decomposition::svd(matrix, true)?;
        Ok(DecompositionResult::Svd { u, s, vt })
    }

    /// Enhanced Cholesky decomposition with numerical stability
    pub fn cholesky_decomposition(&mut self, matrix: &Tensor) -> TorshResult<DecompositionResult> {
        self.validate_square_matrix(matrix, "Cholesky decomposition")?;

        // For now, delegate to existing torsh-linalg implementation
        let l = crate::decomposition::cholesky(matrix, false)?;
        Ok(DecompositionResult::Cholesky { l })
    }

    /// Enhanced eigenvalue decomposition
    pub fn eigenvalue_decomposition(
        &mut self,
        matrix: &Tensor,
        compute_vectors: bool,
    ) -> TorshResult<DecompositionResult> {
        self.validate_square_matrix(matrix, "Eigenvalue decomposition")?;

        // For now, delegate to existing torsh-linalg implementation
        let (values, vectors) = crate::decomposition::eig(matrix)?;
        let vectors = if compute_vectors { Some(vectors) } else { None };
        Ok(DecompositionResult::Eigen { values, vectors })
    }

    /// Solve linear system Ax = b with automatic solver selection
    pub fn solve(&mut self, a: &Tensor, b: &Tensor) -> TorshResult<Tensor> {
        // For now, delegate to existing torsh-linalg implementation
        crate::solvers::core::solve(a, b)
    }

    /// Least squares solver for overdetermined systems
    pub fn least_squares(&mut self, a: &Tensor, b: &Tensor) -> TorshResult<Tensor> {
        // For now, delegate to existing torsh-linalg implementation
        let (solution, _, _, _) = crate::solvers::core::lstsq(a, b, None)?;
        Ok(solution)
    }

    /// Matrix inverse with numerical stability checks
    pub fn inverse(&mut self, matrix: &Tensor) -> TorshResult<Tensor> {
        self.validate_square_matrix(matrix, "Matrix inverse")?;
        // For now, delegate to existing torsh-linalg implementation
        crate::solvers::core::inv(matrix)
    }

    /// Matrix exponential using Padé approximation
    pub fn matrix_exp(&self, matrix: &Tensor) -> TorshResult<Tensor> {
        self.validate_square_matrix(matrix, "Matrix exponential")?;
        // For now, delegate to existing torsh-linalg implementation
        crate::matrix_functions::matrix_exp(matrix)
    }

    /// Matrix logarithm using inverse scaling and squaring
    pub fn matrix_log(&self, matrix: &Tensor) -> TorshResult<Tensor> {
        self.validate_square_matrix(matrix, "Matrix logarithm")?;
        // For now, delegate to existing torsh-linalg implementation
        crate::matrix_functions::matrix_log(matrix)
    }

    /// Matrix square root using Schur decomposition
    pub fn matrix_sqrt(&self, matrix: &Tensor) -> TorshResult<Tensor> {
        self.validate_square_matrix(matrix, "Matrix square root")?;
        // For now, delegate to existing torsh-linalg implementation
        crate::matrix_functions::matrix_sqrt(matrix)
    }

    /// Matrix power A^p for any real power p
    pub fn matrix_pow(&self, matrix: &Tensor, power: f64) -> TorshResult<Tensor> {
        self.validate_square_matrix(matrix, "Matrix power")?;

        // Handle special cases
        if power == 0.0 {
            return torsh_tensor::creation::eye::<f32>(matrix.shape().dims()[0]);
        }
        if power == 1.0 {
            return Ok(matrix.clone());
        }

        // For integer powers, use the existing efficient implementation
        if power.fract() == 0.0 {
            return crate::matrix_functions::matrix_power(matrix, power as i32);
        }

        // For fractional powers, use eigendecomposition: A^p = V * D^p * V^(-1)
        // where A = V * D * V^(-1) is the eigendecomposition
        let (eigenvalues, eigenvectors) = crate::decomposition::eig(matrix)?;

        // Check for negative eigenvalues for fractional powers
        let n = eigenvalues.shape().dims()[0];
        for i in 0..n {
            let eval = eigenvalues.get(&[i])?;
            if eval < 0.0 && power.fract() != 0.0 {
                return Err(TorshError::InvalidArgument(
                    "Matrix power with fractional exponent requires non-negative eigenvalues"
                        .to_string(),
                ));
            }
        }

        // Create diagonal matrix with powered eigenvalues (using same approach as matrix_sqrt)
        let mut powered_diag_data = vec![0.0f32; n * n];
        for i in 0..n {
            let eval = eigenvalues.get(&[i])?;
            let powered_eval = eval.powf(power as f32);
            powered_diag_data[i * n + i] = powered_eval;
        }
        let powered_diag = Tensor::from_data(powered_diag_data, vec![n, n], matrix.device())?;

        // Compute A^p = V * D^p * V^(-1) (using same approach as matrix_sqrt)
        let v_powered_d = eigenvectors.matmul(&powered_diag)?;
        let v_inv = crate::solvers::core::inv(&eigenvectors)?;
        let result = v_powered_d.matmul(&v_inv)?;

        Ok(result)
    }

    /// Compute condition number of matrix
    pub fn condition_number(
        &self,
        matrix: &Tensor,
        _norm_type: MatrixNormType,
    ) -> TorshResult<f64> {
        // For now, use a simple implementation
        let svd_result = self.svd_decomposition_simple(matrix)?;
        if let DecompositionResult::Svd { s, .. } = svd_result {
            let s_data = s.data()? as Vec<f32>;
            if s_data.is_empty() {
                return Ok(f64::INFINITY);
            }
            let max_s = s_data
                .iter()
                .fold(f64::NEG_INFINITY, |a, &b| a.max(b as f64));
            let min_s = s_data.iter().fold(f64::INFINITY, |a, &b| a.min(b as f64));
            Ok(max_s / min_s.max(self.config.tolerance))
        } else {
            Err(TorshError::InvalidArgument(
                "SVD decomposition failed".to_string(),
            ))
        }
    }

    /// Compute matrix rank with numerical tolerance
    pub fn rank(&self, matrix: &Tensor) -> TorshResult<usize> {
        let svd_result = self.svd_decomposition_simple(matrix)?;
        if let DecompositionResult::Svd { s, .. } = svd_result {
            let s_data = s.data()? as Vec<f32>;
            let rank = s_data
                .iter()
                .filter(|&&val| (val as f64) > self.config.tolerance)
                .count();
            Ok(rank)
        } else {
            Err(TorshError::InvalidArgument(
                "SVD decomposition failed".to_string(),
            ))
        }
    }

    /// Matrix norm computation
    pub fn norm(&self, matrix: &Tensor, norm_type: MatrixNormType) -> TorshResult<f64> {
        match norm_type {
            MatrixNormType::Frobenius => {
                let squared = matrix.mul(matrix)?;
                let sum = squared.sum()?;
                Ok((sum.item()? as f64).sqrt())
            }
            MatrixNormType::OneNorm => {
                // Maximum absolute column sum
                let abs_matrix = matrix.abs()?;
                let mut max_col_sum = 0.0f64;
                for j in 0..matrix.shape().dims()[1] {
                    let col = abs_matrix.select(1, j as i64)?;
                    let col_sum = col.sum()?.item()? as f64;
                    max_col_sum = max_col_sum.max(col_sum);
                }
                Ok(max_col_sum)
            }
            MatrixNormType::InfNorm => {
                // Maximum absolute row sum
                let abs_matrix = matrix.abs()?;
                let mut max_row_sum = 0.0f64;
                for i in 0..matrix.shape().dims()[0] {
                    let row = abs_matrix.select(0, i as i64)?;
                    let row_sum = row.sum()?.item()? as f64;
                    max_row_sum = max_row_sum.max(row_sum);
                }
                Ok(max_row_sum)
            }
            MatrixNormType::TwoNorm => {
                // Largest singular value
                let svd_result = self.svd_decomposition_simple(matrix)?;
                if let DecompositionResult::Svd { s, .. } = svd_result {
                    let s_data = s.data()? as Vec<f32>;
                    let max_s = s_data
                        .iter()
                        .fold(f64::NEG_INFINITY, |a, &b| a.max(b as f64));
                    Ok(max_s)
                } else {
                    Err(TorshError::InvalidArgument(
                        "SVD decomposition failed".to_string(),
                    ))
                }
            }
            MatrixNormType::Nuclear => {
                // Sum of singular values
                let svd_result = self.svd_decomposition_simple(matrix)?;
                if let DecompositionResult::Svd { s, .. } = svd_result {
                    let s_data = s.data()? as Vec<f32>;
                    let sum_s: f64 = s_data.iter().map(|&x| x as f64).sum();
                    Ok(sum_s)
                } else {
                    Err(TorshError::InvalidArgument(
                        "SVD decomposition failed".to_string(),
                    ))
                }
            }
        }
    }

    // Helper methods
    fn validate_square_matrix(&self, tensor: &Tensor, operation: &str) -> TorshResult<usize> {
        if tensor.shape().ndim() != 2 {
            return Err(TorshError::InvalidArgument(format!(
                "{} requires a 2D tensor, got {}D tensor",
                operation,
                tensor.shape().ndim()
            )));
        }

        let (rows, cols) = (tensor.shape().dims()[0], tensor.shape().dims()[1]);
        if rows != cols {
            return Err(TorshError::InvalidArgument(format!(
                "{operation} requires a square matrix, got {rows}x{cols} matrix"
            )));
        }

        Ok(rows)
    }

    fn svd_decomposition_simple(&self, matrix: &Tensor) -> TorshResult<DecompositionResult> {
        // Use existing torsh-linalg SVD implementation
        let (u, s, vt) = crate::decomposition::svd(matrix, true)?;
        Ok(DecompositionResult::Svd { u, s, vt })
    }
}

/// Factory function for creating the processor
pub fn create_linalg_processor() -> SciRS2LinalgProcessor {
    SciRS2LinalgProcessor::with_default_config()
}

/// Factory function for high-precision processor
pub fn create_high_precision_processor() -> SciRS2LinalgProcessor {
    SciRS2LinalgProcessor::high_precision()
}

/// Factory function for GPU-accelerated processor
pub fn create_gpu_processor() -> SciRS2LinalgProcessor {
    SciRS2LinalgProcessor::gpu_accelerated()
}

// Export components for external use (commented to avoid re-export issues)
// External crates should import directly from this module

#[cfg(test)]
mod tests {
    use super::*;
    use approx::assert_relative_eq;
    #[test]
    fn test_matrix_pow_integer_power() -> TorshResult<()> {
        let processor = SciRS2LinalgProcessor::with_default_config();

        // Create a simple 2x2 matrix
        let data = vec![2.0f32, 0.0, 0.0, 3.0];
        let matrix = Tensor::from_data(data, vec![2, 2], torsh_core::DeviceType::Cpu)?;

        // Test A^2 for diagonal matrix
        let result = processor.matrix_pow(&matrix, 2.0)?;

        // Expected: [4, 0; 0, 9]
        assert_relative_eq!(result.get(&[0, 0])?, 4.0, epsilon = 1e-6);
        assert_relative_eq!(result.get(&[0, 1])?, 0.0, epsilon = 1e-6);
        assert_relative_eq!(result.get(&[1, 0])?, 0.0, epsilon = 1e-6);
        assert_relative_eq!(result.get(&[1, 1])?, 9.0, epsilon = 1e-6);

        Ok(())
    }

    #[test]
    fn test_matrix_pow_zero_power() -> TorshResult<()> {
        let processor = SciRS2LinalgProcessor::with_default_config();

        // Create a simple 2x2 matrix
        let data = vec![2.0f32, 1.0, 1.0, 2.0];
        let matrix = Tensor::from_data(data, vec![2, 2], torsh_core::DeviceType::Cpu)?;

        // Test A^0 = I
        let result = processor.matrix_pow(&matrix, 0.0)?;

        // Expected: identity matrix
        assert_relative_eq!(result.get(&[0, 0])?, 1.0, epsilon = 1e-6);
        assert_relative_eq!(result.get(&[0, 1])?, 0.0, epsilon = 1e-6);
        assert_relative_eq!(result.get(&[1, 0])?, 0.0, epsilon = 1e-6);
        assert_relative_eq!(result.get(&[1, 1])?, 1.0, epsilon = 1e-6);

        Ok(())
    }

    #[test]
    fn test_matrix_pow_one_power() -> TorshResult<()> {
        let processor = SciRS2LinalgProcessor::with_default_config();

        // Create a simple 2x2 matrix
        let data = vec![2.0f32, 1.0, 1.0, 2.0];
        let matrix = Tensor::from_data(data, vec![2, 2], torsh_core::DeviceType::Cpu)?;

        // Test A^1 = A
        let result = processor.matrix_pow(&matrix, 1.0)?;

        // Expected: original matrix
        assert_relative_eq!(result.get(&[0, 0])?, 2.0, epsilon = 1e-6);
        assert_relative_eq!(result.get(&[0, 1])?, 1.0, epsilon = 1e-6);
        assert_relative_eq!(result.get(&[1, 0])?, 1.0, epsilon = 1e-6);
        assert_relative_eq!(result.get(&[1, 1])?, 2.0, epsilon = 1e-6);

        Ok(())
    }

    #[test]
    fn test_matrix_pow_fractional_positive_definite() -> TorshResult<()> {
        let processor = SciRS2LinalgProcessor::with_default_config();

        // Compare against the existing matrix_sqrt implementation
        let data = vec![4.0f32, 0.0, 0.0, 9.0];
        let matrix = Tensor::from_data(data, vec![2, 2], torsh_core::DeviceType::Cpu)?;

        // Test A^0.5 should match matrix_sqrt(A)
        let result_pow = processor.matrix_pow(&matrix, 0.5)?;
        let result_sqrt = crate::matrix_functions::matrix_sqrt(&matrix)?;

        // Compare the results - they should be approximately equal
        assert_relative_eq!(
            result_pow.get(&[0, 0])?,
            result_sqrt.get(&[0, 0])?,
            epsilon = 1e-3
        );
        assert_relative_eq!(
            result_pow.get(&[0, 1])?,
            result_sqrt.get(&[0, 1])?,
            epsilon = 1e-3
        );
        assert_relative_eq!(
            result_pow.get(&[1, 0])?,
            result_sqrt.get(&[1, 0])?,
            epsilon = 1e-3
        );
        assert_relative_eq!(
            result_pow.get(&[1, 1])?,
            result_sqrt.get(&[1, 1])?,
            epsilon = 1e-3
        );

        Ok(())
    }

    #[test]
    fn test_matrix_pow_invalid_input() -> TorshResult<()> {
        let processor = SciRS2LinalgProcessor::with_default_config();

        // Create non-square matrix
        let data = vec![1.0f32, 2.0, 3.0, 4.0, 5.0, 6.0];
        let nonsquare = Tensor::from_data(data, vec![2, 3], torsh_core::DeviceType::Cpu)?;

        // Should fail for non-square matrix
        assert!(processor.matrix_pow(&nonsquare, 2.0).is_err());

        Ok(())
    }
}
