oxiz_solver/model/

advanced_builder.rs

//! Advanced Model Builder for Theory Combination
//!
//! This module provides sophisticated model construction for CDCL(T) solving,
//! including:
//! - Theory-specific model building
//! - Witness generation for existential quantifiers
//! - Model minimization and optimization
//! - Cross-theory consistency checking

#![allow(dead_code, missing_docs)] // Under development

#[allow(unused_imports)]
use crate::prelude::*;

/// Represents a model assignment for a term
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct ModelValue {
    /// The term being assigned
    pub term: TermId,
    /// The value assigned to the term
    pub value: Value,
    /// The theory that generated this assignment
    pub theory: Theory,
    /// Whether this is a witness for an existential quantifier
    pub is_witness: bool,
}

/// Value types in the model
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum Value {
    /// Boolean value
    Bool(bool),
    /// Integer value
    Int(i64),
    /// Rational value (numerator, denominator)
    Rat(i64, u64),
    /// Bit-vector value (value, width)
    BitVec(u64, usize),
    /// Array value (maps indices to values)
    Array(Box<ArrayValue>),
    /// Uninterpreted function application
    UFApp(String, Vec<Value>),
    /// Datatype constructor
    Constructor(String, Vec<Value>),
}

/// Array value representation
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct ArrayValue {
    /// Default value for unspecified indices
    pub default: Value,
    /// Explicit mappings
    pub mappings: BTreeMap<Value, Value>,
}

/// Theory identifier
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum Theory {
    Core,
    Arithmetic,
    BitVector,
    Array,
    Datatype,
    String,
    Uninterpreted,
}

/// Placeholder term ID
pub type TermId = usize;

/// Statistics for model building
#[derive(Debug, Clone, Default)]
pub struct ModelBuilderStats {
    pub models_built: u64,
    pub witnesses_generated: u64,
    pub consistency_checks: u64,
    pub minimization_steps: u64,
    pub theory_calls: u64,
}

/// Configuration for model building
#[derive(Debug, Clone)]
pub struct ModelBuilderConfig {
    /// Enable model minimization
    pub enable_minimization: bool,
    /// Generate witnesses for existential quantifiers
    pub generate_witnesses: bool,
    /// Check cross-theory consistency
    pub check_consistency: bool,
    /// Maximum number of minimization iterations
    pub max_minimization_iters: usize,
}

impl Default for ModelBuilderConfig {
    fn default() -> Self {
        Self {
            enable_minimization: true,
            generate_witnesses: true,
            check_consistency: true,
            max_minimization_iters: 10,
        }
    }
}

/// Advanced model builder for theory combination
pub struct AdvancedModelBuilder {
    /// Current model assignments
    assignments: FxHashMap<TermId, ModelValue>,
    /// Equivalence classes from equality propagation
    equiv_classes: FxHashMap<TermId, TermId>,
    /// Terms that need witnesses
    witness_obligations: FxHashSet<TermId>,
    /// Theory-specific model fragments
    theory_models: FxHashMap<Theory, TheoryModel>,
    /// Configuration
    config: ModelBuilderConfig,
    /// Statistics
    stats: ModelBuilderStats,
}

/// Theory-specific model fragment
#[derive(Debug, Clone)]
struct TheoryModel {
    /// Assignments specific to this theory
    assignments: FxHashMap<TermId, Value>,
    /// Constraints that must be satisfied
    constraints: Vec<TermId>,
}

impl AdvancedModelBuilder {
    /// Create a new model builder
    pub fn new(config: ModelBuilderConfig) -> Self {
        Self {
            assignments: FxHashMap::default(),
            equiv_classes: FxHashMap::default(),
            witness_obligations: FxHashSet::default(),
            theory_models: FxHashMap::default(),
            config,
            stats: ModelBuilderStats::default(),
        }
    }

    /// Build a model from the current solver state
    pub fn build_model(
        &mut self,
        boolean_assignments: &FxHashMap<TermId, bool>,
        equiv_classes: &FxHashMap<TermId, TermId>,
    ) -> Result<Model, String> {
        self.stats.models_built += 1;
        self.equiv_classes = equiv_classes.clone();

        // Phase 1: Build theory-specific models
        self.build_theory_models(boolean_assignments)?;

        // Phase 2: Generate witnesses for existential quantifiers
        if self.config.generate_witnesses {
            self.generate_witnesses()?;
        }

        // Phase 3: Check cross-theory consistency
        if self.config.check_consistency {
            self.check_cross_theory_consistency()?;
        }

        // Phase 4: Minimize model if requested
        if self.config.enable_minimization {
            self.minimize_model()?;
        }

        // Construct final model
        let model = Model {
            assignments: self.assignments.clone(),
            equiv_classes: self.equiv_classes.clone(),
        };

        Ok(model)
    }

    /// Build theory-specific models
    fn build_theory_models(
        &mut self,
        boolean_assignments: &FxHashMap<TermId, bool>,
    ) -> Result<(), String> {
        // Build arithmetic model
        self.build_arithmetic_model(boolean_assignments)?;

        // Build bit-vector model
        self.build_bitvector_model(boolean_assignments)?;

        // Build array model
        self.build_array_model(boolean_assignments)?;

        // Build datatype model
        self.build_datatype_model(boolean_assignments)?;

        // Build uninterpreted function model
        self.build_uf_model(boolean_assignments)?;

        Ok(())
    }

    /// Build arithmetic model from linear constraints
    fn build_arithmetic_model(
        &mut self,
        boolean_assignments: &FxHashMap<TermId, bool>,
    ) -> Result<(), String> {
        self.stats.theory_calls += 1;

        // Collect arithmetic constraints
        let mut bounds: FxHashMap<TermId, (Option<i64>, Option<i64>)> = FxHashMap::default();

        for (&term, &value) in boolean_assignments {
            if value {
                // Parse constraint and update bounds
                self.update_arithmetic_bounds(term, &mut bounds)?;
            }
        }

        // Generate satisfying assignments
        for (term, (lower, upper)) in bounds {
            let value = match (lower, upper) {
                (Some(l), Some(u)) if l <= u => {
                    // Choose midpoint
                    Value::Int((l + u) / 2)
                }
                (Some(l), None) => Value::Int(l),
                (None, Some(u)) => Value::Int(u),
                (None, None) => Value::Int(0),
                _ => return Err("Inconsistent arithmetic bounds".to_string()),
            };

            self.assignments.insert(
                term,
                ModelValue {
                    term,
                    value,
                    theory: Theory::Arithmetic,
                    is_witness: false,
                },
            );
        }

        Ok(())
    }

    /// Update arithmetic bounds from a constraint
    fn update_arithmetic_bounds(
        &self,
        _term: TermId,
        bounds: &mut FxHashMap<TermId, (Option<i64>, Option<i64>)>,
    ) -> Result<(), String> {
        // Placeholder: Parse term and update bounds
        // In real implementation, would parse constraints like x <= 5, x >= 2
        let var = 0; // Placeholder
        let bound_pair = bounds.entry(var).or_insert((None, None));

        // Example: update lower bound
        if bound_pair.0.is_none_or(|v| v < 0) {
            bound_pair.0 = Some(0);
        }

        Ok(())
    }

    /// Build bit-vector model
    fn build_bitvector_model(
        &mut self,
        _boolean_assignments: &FxHashMap<TermId, bool>,
    ) -> Result<(), String> {
        self.stats.theory_calls += 1;

        // Placeholder: Build satisfying bit-vector assignments
        // Would use bit-blasting results or interval analysis

        Ok(())
    }

    /// Build array model with extensionality
    fn build_array_model(
        &mut self,
        _boolean_assignments: &FxHashMap<TermId, bool>,
    ) -> Result<(), String> {
        self.stats.theory_calls += 1;

        // Placeholder: Build array model
        // Would construct ArrayValue with explicit store operations

        Ok(())
    }

    /// Build datatype model with constructor consistency
    fn build_datatype_model(
        &mut self,
        _boolean_assignments: &FxHashMap<TermId, bool>,
    ) -> Result<(), String> {
        self.stats.theory_calls += 1;

        // Placeholder: Ensure constructor disjointness and injectivity

        Ok(())
    }

    /// Build uninterpreted function model
    fn build_uf_model(
        &mut self,
        _boolean_assignments: &FxHashMap<TermId, bool>,
    ) -> Result<(), String> {
        self.stats.theory_calls += 1;

        // Use equivalence classes to determine function values
        // Functions on equivalent arguments must have equivalent results

        Ok(())
    }

    /// Generate witnesses for existential quantifiers
    fn generate_witnesses(&mut self) -> Result<(), String> {
        let obligations: Vec<_> = self.witness_obligations.iter().copied().collect();

        for term in obligations {
            self.stats.witnesses_generated += 1;

            // Determine the theory and generate an appropriate witness
            let theory = self.determine_theory(term);
            let witness_value = self.generate_theory_witness(term, theory)?;

            self.assignments.insert(
                term,
                ModelValue {
                    term,
                    value: witness_value,
                    theory,
                    is_witness: true,
                },
            );
        }

        Ok(())
    }

    /// Determine which theory a term belongs to
    fn determine_theory(&self, _term: TermId) -> Theory {
        // Placeholder: analyze term structure
        Theory::Core
    }

    /// Generate a witness value for a specific theory
    fn generate_theory_witness(&self, _term: TermId, theory: Theory) -> Result<Value, String> {
        match theory {
            Theory::Arithmetic => Ok(Value::Int(0)),
            Theory::BitVector => Ok(Value::BitVec(0, 32)),
            Theory::Core => Ok(Value::Bool(false)),
            _ => Ok(Value::Int(0)),
        }
    }

    /// Check consistency across theories
    fn check_cross_theory_consistency(&mut self) -> Result<(), String> {
        self.stats.consistency_checks += 1;

        // Check that shared terms have consistent values across theories
        let mut shared_terms: FxHashMap<TermId, Vec<Theory>> = FxHashMap::default();

        for assignment in self.assignments.values() {
            shared_terms
                .entry(assignment.term)
                .or_default()
                .push(assignment.theory);
        }

        for (term, theories) in shared_terms {
            if theories.len() > 1 {
                // Verify all theories agree on the value
                let values: Vec<_> = theories
                    .iter()
                    .filter_map(|&theory| {
                        self.theory_models
                            .get(&theory)
                            .and_then(|m| m.assignments.get(&term))
                    })
                    .collect();

                if values.windows(2).any(|w| w[0] != w[1]) {
                    return Err(format!("Cross-theory inconsistency for term {}", term));
                }
            }
        }

        Ok(())
    }

    /// Minimize the model by removing unnecessary assignments
    fn minimize_model(&mut self) -> Result<(), String> {
        let mut iteration = 0;

        while iteration < self.config.max_minimization_iters {
            self.stats.minimization_steps += 1;
            iteration += 1;

            let initial_size = self.assignments.len();

            // Try to remove each assignment and check if model is still valid
            let terms: Vec<_> = self.assignments.keys().copied().collect();

            for term in terms {
                // Check if this assignment is necessary
                if !self.is_assignment_necessary(term)? {
                    self.assignments.remove(&term);
                }
            }

            // Stop if no progress
            if self.assignments.len() == initial_size {
                break;
            }
        }

        Ok(())
    }

    /// Check if an assignment is necessary for model validity
    fn is_assignment_necessary(&self, _term: TermId) -> Result<bool, String> {
        // Placeholder: would check if removing this assignment
        // causes constraint violations
        Ok(true)
    }

    /// Add a witness obligation for existential quantifier
    pub fn add_witness_obligation(&mut self, term: TermId) {
        self.witness_obligations.insert(term);
    }

    /// Get statistics
    pub fn stats(&self) -> &ModelBuilderStats {
        &self.stats
    }

    /// Reset the builder
    pub fn reset(&mut self) {
        self.assignments.clear();
        self.equiv_classes.clear();
        self.witness_obligations.clear();
        self.theory_models.clear();
    }
}

/// Complete model with all assignments
#[derive(Debug, Clone)]
pub struct Model {
    /// All term assignments
    pub assignments: FxHashMap<TermId, ModelValue>,
    /// Equivalence classes
    pub equiv_classes: FxHashMap<TermId, TermId>,
}

impl Model {
    /// Evaluate a term in this model
    pub fn eval(&self, term: TermId) -> Option<&Value> {
        // First check direct assignment
        if let Some(assignment) = self.assignments.get(&term) {
            return Some(&assignment.value);
        }

        // Check equivalence class representative
        if let Some(&repr) = self.equiv_classes.get(&term)
            && let Some(assignment) = self.assignments.get(&repr)
        {
            return Some(&assignment.value);
        }

        None
    }

    /// Check if a boolean term is true in this model
    pub fn is_true(&self, term: TermId) -> bool {
        matches!(self.eval(term), Some(Value::Bool(true)))
    }

    /// Get all assignments for a specific theory
    pub fn theory_assignments(&self, theory: Theory) -> Vec<&ModelValue> {
        self.assignments
            .values()
            .filter(|v| v.theory == theory)
            .collect()
    }

    /// Get witness assignments
    pub fn witnesses(&self) -> Vec<&ModelValue> {
        self.assignments.values().filter(|v| v.is_witness).collect()
    }
}

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

    #[test]
    fn test_model_builder_creation() {
        let config = ModelBuilderConfig::default();
        let builder = AdvancedModelBuilder::new(config);
        assert_eq!(builder.stats.models_built, 0);
    }

    #[test]
    fn test_arithmetic_model_simple() {
        let config = ModelBuilderConfig::default();
        let mut builder = AdvancedModelBuilder::new(config);

        let assignments = FxHashMap::default();
        let equiv_classes = FxHashMap::default();

        let result = builder.build_model(&assignments, &equiv_classes);
        assert!(result.is_ok());
        assert_eq!(builder.stats.models_built, 1);
    }

    #[test]
    fn test_witness_generation() {
        let config = ModelBuilderConfig {
            generate_witnesses: true,
            ..Default::default()
        };
        let mut builder = AdvancedModelBuilder::new(config);

        builder.add_witness_obligation(42);
        assert!(builder.witness_obligations.contains(&42));

        let result = builder.generate_witnesses();
        assert!(result.is_ok());
        assert_eq!(builder.stats.witnesses_generated, 1);
    }

    #[test]
    fn test_model_evaluation() {
        let mut assignments = FxHashMap::default();
        assignments.insert(
            1,
            ModelValue {
                term: 1,
                value: Value::Int(42),
                theory: Theory::Arithmetic,
                is_witness: false,
            },
        );

        let model = Model {
            assignments,
            equiv_classes: FxHashMap::default(),
        };

        assert_eq!(model.eval(1), Some(&Value::Int(42)));
        assert_eq!(model.eval(2), None);
    }

    #[test]
    fn test_model_with_equivalence() {
        let mut assignments = FxHashMap::default();
        assignments.insert(
            1,
            ModelValue {
                term: 1,
                value: Value::Int(42),
                theory: Theory::Arithmetic,
                is_witness: false,
            },
        );

        let mut equiv_classes = FxHashMap::default();
        equiv_classes.insert(2, 1); // 2 is equivalent to 1

        let model = Model {
            assignments,
            equiv_classes,
        };

        assert_eq!(model.eval(2), Some(&Value::Int(42)));
    }

    #[test]
    fn test_theory_assignments_filter() {
        let mut assignments = FxHashMap::default();
        assignments.insert(
            1,
            ModelValue {
                term: 1,
                value: Value::Int(42),
                theory: Theory::Arithmetic,
                is_witness: false,
            },
        );
        assignments.insert(
            2,
            ModelValue {
                term: 2,
                value: Value::BitVec(0xff, 8),
                theory: Theory::BitVector,
                is_witness: false,
            },
        );

        let model = Model {
            assignments,
            equiv_classes: FxHashMap::default(),
        };

        let arith_assignments = model.theory_assignments(Theory::Arithmetic);
        assert_eq!(arith_assignments.len(), 1);
        assert_eq!(arith_assignments[0].term, 1);
    }

    #[test]
    fn test_witness_filter() {
        let mut assignments = FxHashMap::default();
        assignments.insert(
            1,
            ModelValue {
                term: 1,
                value: Value::Int(0),
                theory: Theory::Arithmetic,
                is_witness: true,
            },
        );
        assignments.insert(
            2,
            ModelValue {
                term: 2,
                value: Value::Int(5),
                theory: Theory::Arithmetic,
                is_witness: false,
            },
        );

        let model = Model {
            assignments,
            equiv_classes: FxHashMap::default(),
        };

        let witnesses = model.witnesses();
        assert_eq!(witnesses.len(), 1);
        assert_eq!(witnesses[0].term, 1);
    }

    #[test]
    fn test_consistency_check() {
        let config = ModelBuilderConfig {
            check_consistency: true,
            ..Default::default()
        };
        let mut builder = AdvancedModelBuilder::new(config);

        let result = builder.check_cross_theory_consistency();
        assert!(result.is_ok());
        assert_eq!(builder.stats.consistency_checks, 1);
    }

    #[test]
    fn test_minimization() {
        let config = ModelBuilderConfig {
            enable_minimization: true,
            max_minimization_iters: 5,
            ..Default::default()
        };
        let mut builder = AdvancedModelBuilder::new(config);

        // Add some assignments
        builder.assignments.insert(
            1,
            ModelValue {
                term: 1,
                value: Value::Int(42),
                theory: Theory::Arithmetic,
                is_witness: false,
            },
        );

        let result = builder.minimize_model();
        assert!(result.is_ok());
        assert!(builder.stats.minimization_steps > 0);
    }

    #[test]
    fn test_reset() {
        let mut builder = AdvancedModelBuilder::new(ModelBuilderConfig::default());

        builder.assignments.insert(
            1,
            ModelValue {
                term: 1,
                value: Value::Int(42),
                theory: Theory::Arithmetic,
                is_witness: false,
            },
        );
        builder.add_witness_obligation(2);

        builder.reset();
        assert!(builder.assignments.is_empty());
        assert!(builder.witness_obligations.is_empty());
    }
}