/// Demonstration of what happens when precision_digits is missing/ignored
/// This would show exactly the bug that was in the original implementation

use selen::prelude::*;
use selen::optimization::subproblem_solving::{FloatSubproblemSolver, SubproblemValue};
use selen::optimization::variable_partitioning::VariablePartition;

#[test]
fn test_broken_implementation_simulation() {
    println!("=== Simulating the Bug: Ignoring Precision ===");
    
    // This test simulates what would happen if FloatSubproblemSolver
    // ignored the precision_digits field and just used arbitrary arithmetic
    
    let mut model = Model::with_float_precision(1); // 1 decimal = 0.1 steps
    let var_id = model.float(0.0, 1.0).into();
    
    println!("Model expects: 1 decimal precision (0.1 steps)");
    
    let partition = VariablePartition {
        float_variables: vec![var_id],
        integer_variables: vec![],
        constraint_count: 0,
    };
    
    // The broken implementation would create a solver but ignore the precision parameter
    let solver = FloatSubproblemSolver::new(1); // Parameter ignored in broken version
    
    let result = solver.solve_float_subproblem(&model, &partition).expect("Should solve");
    
    if let Some(SubproblemValue::Float(value)) = result.variable_assignments.get(&var_id) {
        println!("Actual solution: {}", value);
        
        // In the CURRENT (fixed) implementation, this should pass
        let step_size = 0.1;
        let remainder = value % step_size;
        println!("Remainder when divided by expected step (0.1): {}", remainder);
        
        // With the BROKEN implementation (ignoring precision_digits), we would get something like:
        // - Midpoint calculation: (0.0 + 1.0) / 2.0 = 0.5
        // - No rounding to step boundaries
        // - Result: exactly 0.5, which IS aligned to 0.1 steps (lucky!)
        // 
        // But with bounds like 0.33 to 0.67, we'd get:
        // - Midpoint: (0.33 + 0.67) / 2.0 = 0.5  
        // - Still happens to align!
        //
        // So we need a test case that would definitely NOT align...
        
        // The current implementation should respect precision
        assert!(remainder.abs() < 1e-10 || (step_size - remainder).abs() < 1e-10,
            "Current implementation should respect precision");
    }
}

#[test]
fn test_case_that_would_definitely_expose_bug() {
    println!("\n=== Test Case That Would Definitely Expose Missing Precision ===");
    
    // Use bounds that would produce a non-aligned midpoint if precision is ignored
    let mut model = Model::with_float_precision(2); // 2 decimals = 0.01 steps
    
    // Bounds chosen so midpoint doesn't align to 0.01 boundaries
    let var_id = model.float(0.333, 0.336).into(); // Midpoint: 0.3345
    
    println!("Bounds: 0.333 to 0.336");
    println!("Expected precision: 2 decimals (0.01 steps)");
    println!("Midpoint if precision ignored: 0.3345");
    println!("0.3345 % 0.01 = {}", 0.3345 % 0.01);
    println!("^ This non-zero remainder would expose the bug!");
    
    let partition = VariablePartition {
        float_variables: vec![var_id],
        integer_variables: vec![],
        constraint_count: 0,
    };
    
    let solver = FloatSubproblemSolver::new(2);
    let result = solver.solve_float_subproblem(&model, &partition).expect("Should solve");
    
    if let Some(SubproblemValue::Float(value)) = result.variable_assignments.get(&var_id) {
        println!("Actual solution: {}", value);
        
        let step_size = 0.01;
        let remainder = value % step_size;
        println!("Remainder: {}", remainder);
        
        // With BROKEN implementation: remainder would be ~0.0045 (non-zero)
        // With FIXED implementation: remainder should be ~0 (properly aligned)
        
        assert!(remainder.abs() < 1e-10 || (step_size - remainder).abs() < 1e-10,
            "Fixed implementation should align to precision boundaries");
            
        // Additional check: solution should be 0.33 or 0.34 (the valid 2-decimal values in range)
        assert!((value - 0.33).abs() < 1e-10 || (value - 0.34).abs() < 1e-10,
            "Solution should be exactly 0.33 or 0.34 for 2-decimal precision in range [0.333, 0.336]");
            
        println!("✓ Fixed implementation correctly rounds to precision boundaries");
    }
}

#[test]
fn test_how_bug_would_manifest_in_practice() {
    println!("\n=== How the Bug Would Manifest in Real Usage ===");
    
    // Show how the missing precision would cause problems in constraint systems
    let mut model = Model::with_float_precision(3); // 3 decimals = 0.001 steps
    
    // Create variables with bounds that don't align nicely
    let x = model.float(1.2345, 1.2347).into(); // Tiny range with non-aligned bounds
    
    println!("Variable bounds: 1.2345 to 1.2347");
    println!("Expected precision: 3 decimals (0.001 steps)");
    println!("Valid solutions: 1.235, 1.236, 1.237");
    
    let partition = VariablePartition {
        float_variables: vec![x],
        integer_variables: vec![],
        constraint_count: 0,
    };
    
    let solver = FloatSubproblemSolver::new(3);
    let result = solver.solve_float_subproblem(&model, &partition).expect("Should solve");
    
    if let Some(SubproblemValue::Float(value)) = result.variable_assignments.get(&x) {
        println!("Solution: {}", value);
        
        // Check if it's one of the valid 3-decimal values in range
        let valid_values = vec![1.235, 1.236, 1.237];
        let is_valid = valid_values.iter().any(|&valid| (value - valid).abs() < 1e-12);
        
        println!("Is solution one of valid 3-decimal values? {}", is_valid);
        
        if !is_valid {
            println!("BROKEN: Solution {} is not aligned to 3-decimal precision!", value);
            println!("This would cause issues in constraint systems expecting aligned values!");
        }
        
        assert!(is_valid, "Solution must be aligned to model precision");
        println!("✓ Solution correctly aligned to 3-decimal precision");
    }
}