// SPRINT2-001: TDD tests for property-based testing
// Following Toyota Way: Write tests first, then implementation
//
// NOTE: Currently disabled - testing APIs that don't exist or have changed

/*
use ruchy::notebook::testing::*;
use proptest::prelude::*;
use std::collections::HashSet;

proptest! {
    #[test]
    fn test_notebook_execution_deterministic(
        seed: u64,
        num_cells in 1..20usize
    ) {
        // Property: Same notebook executed twice should give same results
        let notebook = generate_test_notebook(seed, num_cells);
        
        let mut tester1 = NotebookTester::new();
        let mut tester2 = NotebookTester::new();
        
        let results1: Vec<_> = notebook.cells.iter()
            .filter(|c| matches!(c.cell_type, CellType::Code))
            .map(|cell| tester1.execute_cell(cell))
            .collect();
            
        let results2: Vec<_> = notebook.cells.iter()
            .filter(|c| matches!(c.cell_type, CellType::Code))
            .map(|cell| tester2.execute_cell(cell))
            .collect();
            
        // Verify deterministic execution
        assert_eq!(results1.len(), results2.len());
        for (r1, r2) in results1.iter().zip(results2.iter()) {
            match (r1, r2) {
                (Ok(o1), Ok(o2)) => assert_eq!(o1, o2),
                (Err(e1), Err(e2)) => assert_eq!(e1, e2),
                _ => panic!("Execution not deterministic"),
            }
        }
    }
    
    #[test]
    fn test_state_isolation_property(
        cells in prop::collection::vec(any::<String>(), 1..10)
    ) {
        // Property: Cell execution should not affect unrelated cells
        let mut tester = NotebookTester::new();
        
        // Execute cells that define variables
        for (i, source) in cells.iter().enumerate() {
            if source.trim().is_empty() { continue; }
            
            let cell = Cell {
                id: format!("cell_{}", i),
                source: format!("let var_{} = {}", i, i),
                cell_type: CellType::Code,
                metadata: CellMetadata::default(),
            };
            
            let _ = tester.execute_cell(&cell);
        }
        
        // Property: Each variable should be independent
        let test_cell = Cell {
            id: "test".to_string(),
            source: "1 + 1".to_string(),
            cell_type: CellType::Code,
            metadata: CellMetadata::default(),
        };
        
        let result = tester.execute_cell(&test_cell);
        assert!(result.is_ok());
        
        match result.unwrap() {
            CellOutput::Value(v) => assert_eq!(v, "2"),
            _ => panic!("Expected simple arithmetic to work"),
        }
    }
    
    #[test]
    fn test_golden_file_roundtrip(
        outputs in prop::collection::vec(
            prop_oneof![
                Just(CellOutput::Value("42".to_string())),
                Just(CellOutput::Error("error".to_string())),
                Just(CellOutput::Html("<div>test</div>".to_string())),
            ],
            1..20
        )
    ) {
        // Property: Save and load should preserve outputs
        let temp_dir = tempfile::TempDir::new().unwrap();
        let manager = GoldenManager::new(temp_dir.path());
        
        // Save all outputs
        for (i, output) in outputs.iter().enumerate() {
            let path = format!("golden_{}.txt", i);
            manager.save_golden(std::path::Path::new(&path), output)
                .expect("Failed to save golden");
        }
        
        // Load and verify
        for (i, expected) in outputs.iter().enumerate() {
            let path = format!("golden_{}.txt", i);
            let loaded = manager.load_golden(std::path::Path::new(&path))
                .expect("Failed to load golden");
            
            // Note: DataFrames and complex types are serialized as debug format
            // and loaded as Value, so we need to handle that
            match (expected, &loaded) {
                (CellOutput::Value(e), CellOutput::Value(l)) => assert_eq!(e, l),
                (CellOutput::Error(e), CellOutput::Value(l)) => assert_eq!(e, l),
                (CellOutput::Html(e), CellOutput::Value(l)) => assert_eq!(e, l),
                _ => {} // Other conversions are acceptable
            }
        }
    }
    
    #[test]
    fn test_numeric_tolerance_property(
        base in 0.0..1000.0f64,
        delta in -0.1..0.1f64,
        tolerance in 0.0..1.0f64
    ) {
        // Property: Comparison with tolerance should be symmetric
        let tester = NotebookTester::new();
        
        let output1 = CellOutput::Value(base.to_string());
        let output2 = CellOutput::Value((base + delta).to_string());
        
        let result1 = tester.compare_outputs(&output1, &output2, Some(tolerance));
        let result2 = tester.compare_outputs(&output2, &output1, Some(tolerance));
        
        // Symmetric property
        match (&result1, &result2) {
            (TestResult::Pass, TestResult::Pass) => {
                assert!(delta.abs() <= tolerance);
            }
            (TestResult::NumericDivergence { .. }, TestResult::NumericDivergence { .. }) => {
                assert!(delta.abs() > tolerance);
            }
            _ => {} // Other combinations acceptable
        }
    }
    
    #[test]
    fn test_dataframe_comparison_property(
        rows in 1..10usize,
        cols in 1..5usize,
        seed: u64
    ) {
        // Property: Identical DataFrames should always match
        let df = generate_test_dataframe(seed, rows, cols);
        let output1 = CellOutput::DataFrame(df.clone());
        let output2 = CellOutput::DataFrame(df);
        
        let tester = NotebookTester::new();
        let result = tester.compare_dataframes(&output1, &output2, 0.0);
        
        assert_eq!(result, TestResult::Pass);
    }
    
    #[test]
    fn test_coverage_accumulation_property(
        num_cells in 1..50usize
    ) {
        // Property: Coverage should never decrease
        let tracker = CoverageTracker::new();
        let mut prev_coverage = 0.0;
        
        for i in 0..num_cells {
            let cell = Cell {
                id: format!("cell_{}", i),
                source: format!("let x{} = {}", i, i),
                cell_type: CellType::Code,
                metadata: CellMetadata::default(),
            };
            
            let instrumented = tracker.instrument_cell(&cell);
            tracker.execute_instrumented(&instrumented, &cell.source);
            
            let report = tracker.report_coverage();
            
            // Coverage should be monotonic (never decrease)
            assert!(report.line_coverage >= prev_coverage || 
                   (report.line_coverage - prev_coverage).abs() < f64::EPSILON);
            prev_coverage = report.line_coverage;
        }
    }
}

// Helper function to generate test notebooks
fn generate_test_notebook(seed: u64, num_cells: usize) -> Notebook {
    use rand::{Rng, SeedableRng};
    use rand::rngs::StdRng;
    
    let mut rng = StdRng::seed_from_u64(seed);
    let mut cells = Vec::new();
    
    for i in 0..num_cells {
        let cell_type = if rng.gen_bool(0.8) {
            CellType::Code
        } else {
            CellType::Markdown
        };
        
        let source = match cell_type {
            CellType::Code => {
                match rng.gen_range(0..5) {
                    0 => format!("let x{} = {}", i, rng.gen_range(0..100)),
                    1 => format!("{} + {}", rng.gen_range(0..10), rng.gen_range(0..10)),
                    2 => format!("println(\"Cell {}\")", i),
                    3 => "1 + 1".to_string(),
                    _ => format!("// Comment {}", i),
                }
            }
            CellType::Markdown => format!("# Heading {}\n\nSome text.", i),
        };
        
        cells.push(Cell {
            id: format!("cell_{}", i),
            source,
            cell_type,
            metadata: CellMetadata::default(),
        });
    }
    
    Notebook {
        cells,
        metadata: None,
    }
}

// Helper function to generate test DataFrames
fn generate_test_dataframe(seed: u64, rows: usize, cols: usize) -> DataFrameData {
    use rand::{Rng, SeedableRng};
    use rand::rngs::StdRng;
    
    let mut rng = StdRng::seed_from_u64(seed);
    
    let columns: Vec<String> = (0..cols)
        .map(|i| format!("col_{}", i))
        .collect();
        
    let rows: Vec<Vec<String>> = (0..rows)
        .map(|_| {
            (0..cols)
                .map(|_| rng.gen_range(0..100).to_string())
                .collect()
        })
        .collect();
        
    DataFrameData { columns, rows }
}

#[test]
fn test_property_testing_infrastructure() {
    // Basic test to ensure property testing is set up
    proptest!(|(x in 0..100)| {
        assert!(x < 100);
    });
}*/
