theory_core 0.1.0

//! Solver Surrogate: Fast Neural Approximation of SMT Solvers
//!
//! Provides fast neural network approximations of SMT solver results
//! for speculation in the speculate-then-verify pipeline.

use crate::ir::ConstraintIR;
use anyhow::Result;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;

/// Result of solving constraints
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SolutionResult {
    /// Whether the constraints are satisfiable
    pub satisfiable: bool,
    /// Variable assignments (if satisfiable)
    pub assignment: HashMap<String, SolutionValue>,
    /// Confidence in the solution (0.0 to 1.0)
    pub confidence: f32,
    /// Estimated solving time in milliseconds
    pub estimated_time_ms: f64,
    /// Method used to obtain the solution
    pub method: String,
    /// ID of the branch that found this solution (for Lightning Strike analytics)
    pub winning_branch_id: Option<String>,
    /// Strategy used by the winning branch (Neural, SMT, Speculate, etc.)
    pub winning_strategy: Option<String>,
    /// Backend used by the winning branch (CPU, GPU, etc.)  
    pub winning_backend: Option<String>,
    /// Range information for variables with inequality constraints
    #[serde(skip_serializing_if = "Option::is_none")]
    pub variable_ranges: Option<HashMap<String, String>>,
}

/// Values assigned to variables in solutions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SolutionValue {
    Bool(bool),
    Integer(i64),
    Real(f64),
    String(String),
    BitVector { value: u64, width: u32 },
}

/// Trait for fast neural approximation of SMT solvers
pub trait SolverSurrogate: Send + Sync {
    /// Predict solver result from constraint IR
    fn predict_solution(&self, constraints: &ConstraintIR) -> Result<SolutionResult>;

    /// Get confidence in prediction (0.0 to 1.0)
    fn prediction_confidence(&self, constraints: &ConstraintIR) -> Result<f32>;

    /// Update surrogate based on actual solver results
    fn update_from_solver_result(
        &mut self,
        constraints: &ConstraintIR,
        actual_result: &SolutionResult,
    ) -> Result<()>;

    /// Estimate solving time for given constraints
    fn estimate_solving_time(&self, constraints: &ConstraintIR) -> Result<f64>;
}

/// Configuration for neural solver surrogate
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SurrogateConfig {
    /// Architecture configuration
    pub encoder_dims: Vec<usize>,
    pub decoder_dims: Vec<usize>,
    /// Training parameters
    pub learning_rate: f32,
    pub batch_size: usize,
    /// Confidence thresholds
    pub min_confidence: f32,
    pub fallback_threshold: f32,
}

impl Default for SurrogateConfig {
    fn default() -> Self {
        Self {
            encoder_dims: vec![256, 128, 64],
            decoder_dims: vec![64, 128, 256],
            learning_rate: 0.001,
            batch_size: 32,
            min_confidence: 0.7,
            fallback_threshold: 0.5,
        }
    }
}

/// Neural network-based solver surrogate
pub struct NeuralSolverSurrogate {
    config: SurrogateConfig,
    // TODO: Add actual neural network components
}

impl NeuralSolverSurrogate {
    pub fn new(config: SurrogateConfig) -> Self {
        Self { config }
    }
}

impl SolverSurrogate for NeuralSolverSurrogate {
    fn predict_solution(&self, constraints: &ConstraintIR) -> Result<SolutionResult> {
        // TODO: Implement actual neural network inference
        // For now, return a dummy solution based on constraint analysis

        let mut assignment = HashMap::new();

        // Generate assignments for all variables
        for (var_id, variable) in &constraints.variables {
            let value = match &variable.domain {
                crate::ir::Domain::Boolean => SolutionValue::Bool(true),
                crate::ir::Domain::Integer { min, max: _ } => {
                    SolutionValue::Integer(min.unwrap_or(0))
                }
                crate::ir::Domain::Real { min, max: _ } => SolutionValue::Real(min.unwrap_or(0.0)),
                crate::ir::Domain::BitVector { width } => SolutionValue::BitVector {
                    value: 0,
                    width: *width,
                },
                crate::ir::Domain::String { max_length: _ } => {
                    SolutionValue::String("default".to_string())
                }
                _ => SolutionValue::Integer(0),
            };

            assignment.insert(format!("var_{}", var_id.0), value);
        }

        Ok(SolutionResult {
            satisfiable: true,
            assignment,
            confidence: 0.8,
            estimated_time_ms: 10.0,
            method: "StubSurrogate".to_string(),
            winning_branch_id: Some("DemoSurrogate".to_string()),
            winning_strategy: Some("Neural".to_string()),
            winning_backend: Some("CPU".to_string()),
            variable_ranges: None,
        })
    }

    fn prediction_confidence(&self, _constraints: &ConstraintIR) -> Result<f32> {
        // TODO: Implement confidence estimation based on constraint features
        Ok(0.8)
    }

    fn update_from_solver_result(
        &mut self,
        _constraints: &ConstraintIR,
        _actual_result: &SolutionResult,
    ) -> Result<()> {
        // TODO: Implement learning from actual solver results
        Ok(())
    }

    fn estimate_solving_time(&self, constraints: &ConstraintIR) -> Result<f64> {
        // Simple heuristic based on number of constraints and variables
        let num_vars = constraints.variables.len();
        let num_constraints = constraints.constraints.len();

        // Rough estimate in milliseconds
        let base_time = 1.0;
        let var_factor = num_vars as f64 * 0.1;
        let constraint_factor = num_constraints as f64 * 0.5;

        Ok(base_time + var_factor + constraint_factor)
    }
}