//! ternlang-mkl: Core Implementation of Ternary Math Kernels
//! Establishing the "cuTern" standard for sparse matrix operations.

pub mod tensor {
    use std::fmt;

    #[derive(Clone, Copy, Debug, PartialEq, Eq)]
    #[repr(i8)]
    pub enum Trit {
        Pos = 1,
        Zero = 0,
        Neg = -1,
    }

    impl fmt::Display for Trit {
        fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
            match self {
                Trit::Pos => write!(f, "+1"),
                Trit::Zero => write!(f, " 0"),
                Trit::Neg => write!(f, "-1"),
            }
        }
    }

    pub struct TernaryTensor {
        pub data: Vec<i8>,
        pub shape: (usize, usize),
    }

    impl TernaryTensor {
        pub fn new(rows: usize, cols: usize, data: Vec<i8>) -> Self {
            assert_eq!(data.len(), rows * cols, "Data size must match shape");
            TernaryTensor { data, shape: (rows, cols) }
        }

        /// The cuTern "Sparse Multiply" Implementation.
        /// Physically skips the hardware cycle if either operand is Trit::Zero (0).
        pub fn sparse_matmul(&self, other: &Self) -> Result<Self, String> {
            if self.shape.1 != other.shape.0 {
                return Err(format!("Shape mismatch: {:?} vs {:?}", self.shape, other.shape));
            }

            let (m, k) = self.shape;
            let n = other.shape.1;
            let mut result_data = vec![0i8; m * n];
            let mut cycles_saved = 0;
            let total_theoretical_cycles = m * n * k;

            for i in 0..m {
                for j in 0..n {
                    let mut sum: i32 = 0;
                    for p in 0..k {
                        let a = self.data[i * k + p];
                        let b = other.data[p * n + j];

                        // THE BET VM HARDWARE BYPASS SIMULATION
                        if a == 0 || b == 0 {
                            cycles_saved += 1;
                            continue; // Logic-level bypass: No arithmetic instruction issued
                        }

                        sum += (a as i32) * (b as i32);
                    }
                    // Map result back to ternary space (clamping for simplicity in this V1)
                    result_data[i * n + j] = if sum > 0 { 1 } else if sum < 0 { -1 } else { 0 };
                }
            }

            let sparsity_yield = (cycles_saved as f64 / total_theoretical_cycles as f64) * 100.0;
            println!("cuTern Trace: Sparse MatMul Complete. Saved {} cycles ({:.2}% efficiency gain).", 
                     cycles_saved, sparsity_yield);

            Ok(TernaryTensor { data: result_data, shape: (m, n) })
        }

        pub fn display(&self) {
            for i in 0..self.shape.0 {
                for j in 0..self.shape.1 {
                    let val = self.data[i * self.shape.1 + j];
                    print!("{} ", if val == 1 {"+1"} else if val == -1 {"-1"} else {" 0"});
                }
                println!();
            }
        }
    }
}

pub mod risk {
    use super::tensor::TernaryTensor;

    /// High-level Enterprise Underwriting Kernel.
    /// Securely routes financial decisions without binary if/else coercion.
    pub fn evaluate_credit_risk(profile: &TernaryTensor, historical_vetoes: &TernaryTensor) -> i8 {
        // Mocking an MoE Deliberation result
        let result = profile.sparse_matmul(historical_vetoes);
        match result {
            Ok(res) => {
                let sum: i32 = res.data.iter().map(|&x| x as i32).sum();
                if sum > 5 { 1 } else if sum < -5 { -1 } else { 0 }
            }
            Err(_) => 0, // Default to Safe Hold (State 0)
        }
    }
}
