//! Tests for PINN training implementation
//!
//! These tests verify the correctness and robustness of the Physics-Informed
//! Neural Network training algorithms.

#[cfg(test)]
mod tests {
    use super::super::{PINNTrainer, TrainingConfig, LossComponents};
    use crate::physics::{PINN, BoundaryConditions, HeatEquation};
    use crate::error::Result;

    #[tokio::test]
    async fn test_training_config_defaults() {
        let config = TrainingConfig::default();

        assert_eq!(config.learning_rate, 1e-3);
        assert_eq!(config.physics_weight, 1.0);
        assert_eq!(config.boundary_weight, 100.0);
        assert_eq!(config.convergence_tolerance, 1e-6);
        assert_eq!(config.patience, 100);
        assert_eq!(config.conservation_weights, vec![1.0]);
    }

    #[tokio::test]
    async fn test_trainer_creation() {
        let config = TrainingConfig::default();
        let trainer = PINNTrainer::new(config);

        // Verify trainer is created successfully
        assert!(trainer.config.learning_rate > 0.0);
    }

    #[tokio::test]
    async fn test_latin_hypercube_sampling() -> Result<()> {
        let config = TrainingConfig::default();
        let trainer = PINNTrainer::new(config);

        // Create mock boundary conditions
        let boundary_data = create_test_boundary_conditions();

        // Generate training points
        let points = trainer.generate_training_points(&boundary_data, 100)?;

        // Verify correct number of points
        assert_eq!(points.len(), 100);

        // Verify points are within domain bounds
        let (x_min, x_max) = boundary_data.spatial_bounds();
        let (t_min, t_max) = boundary_data.temporal_bounds();

        for (x, t) in &points {
            assert!(*x >= x_min && *x <= x_max, "x coordinate {} out of bounds [{}, {}]", x, x_min, x_max);
            assert!(*t >= t_min && *t <= t_max, "t coordinate {} out of bounds [{}, {}]", t, t_min, t_max);
        }

        // Verify good space coverage (no clustering)
        let mut x_coords: Vec<f64> = points.iter().map(|(x, _)| *x).collect();
        x_coords.sort_by(|a, b| a.total_cmp(b));

        // Check that points are reasonably spread out
        let min_spacing = (x_max - x_min) / 200.0; // Expect at least this spacing
        for i in 1..x_coords.len() {
            let spacing = x_coords[i] - x_coords[i-1];
            if spacing < min_spacing {
                // Allow some clustering but not excessive
                let clustered_count = x_coords.windows(2)
                    .filter(|w| w[1] - w[0] < min_spacing)
                    .count();
                assert!(clustered_count < points.len() / 4, "Too much point clustering detected");
                break;
            }
        }

        Ok(())
    }

    #[tokio::test]
    async fn test_boundary_point_sampling() -> Result<()> {
        let config = TrainingConfig::default();
        let trainer = PINNTrainer::new(config);

        let boundary_data = create_test_boundary_conditions();
        let points = trainer.sample_boundary_points(&boundary_data, 100)?;

        // Verify correct number of points
        assert_eq!(points.len(), 100);

        let (x_min, x_max) = boundary_data.spatial_bounds();
        let (t_min, t_max) = boundary_data.temporal_bounds();

        // Count points on each boundary
        let mut left_boundary = 0;
        let mut right_boundary = 0;
        let mut bottom_boundary = 0;
        let mut top_boundary = 0;

        for (x, t) in &points {
            if (*x - x_min).abs() < 1e-10 { left_boundary += 1; }
            if (*x - x_max).abs() < 1e-10 { right_boundary += 1; }
            if (*t - t_min).abs() < 1e-10 { bottom_boundary += 1; }
            if (*t - t_max).abs() < 1e-10 { top_boundary += 1; }
        }

        // Verify roughly equal distribution on boundaries
        let expected_per_boundary = 100 / 4;
        assert!(left_boundary >= expected_per_boundary - 2);
        assert!(right_boundary >= expected_per_boundary - 2);
        assert!(bottom_boundary >= expected_per_boundary - 2);
        assert!(top_boundary >= expected_per_boundary - 2);

        Ok(())
    }

    #[tokio::test]
    async fn test_convergence_detection() -> Result<()> {
        let mut config = TrainingConfig::default();
        config.convergence_tolerance = 1e-4;
        config.patience = 10;

        let trainer = PINNTrainer::new(config);

        // Test converged case
        let mut loss_history = std::collections::VecDeque::new();
        for _ in 0..15 {
            loss_history.push_back(1e-5); // Loss below tolerance
        }

        let converged = trainer.check_convergence(&loss_history, 1e-5)?;
        assert!(converged, "Should detect convergence when loss is below tolerance");

        // Test non-converged case
        loss_history.clear();
        for i in 0..15 {
            loss_history.push_back(0.1 - (i as f64) * 0.001); // Steadily decreasing but above tolerance
        }

        let converged = trainer.check_convergence(&loss_history, 0.085)?;
        assert!(!converged, "Should not detect convergence when loss is above tolerance");

        Ok(())
    }

    #[tokio::test]
    async fn test_adam_optimizer() -> Result<()> {
        use super::super::AdamOptimizer;

        let mut optimizer = AdamOptimizer::new(0.001);

        // Test initial state
        assert!((optimizer.learning_rate - 0.001).abs() < 1e-10);

        // Test learning rate update
        optimizer.set_learning_rate(0.01);
        assert!((optimizer.learning_rate - 0.01).abs() < 1e-10);

        // Test optimizer step (mock parameters)
        let mock_params = vec![];
        let result = optimizer.step(mock_params).await;
        assert!(result.is_ok(), "Optimizer step should succeed");

        Ok(())
    }

    #[tokio::test]
    async fn test_cosine_annealing_scheduler() -> Result<()> {
        use super::super::CosineAnnealingScheduler;

        let scheduler = CosineAnnealingScheduler::new(0.01, 100, 0.001);

        // Test initial learning rate
        let lr_0 = scheduler.step(0);
        assert!((lr_0 - 0.01).abs() < 1e-6, "Initial LR should be close to max");

        // Test middle learning rate
        let lr_50 = scheduler.step(50);
        assert!(lr_50 < 0.01 && lr_50 > 0.001, "Middle LR should be between min and max");

        // Test final learning rate
        let lr_100 = scheduler.step(100);
        assert!((lr_100 - 0.001).abs() < 1e-6, "Final LR should be close to min");

        // Test monotonic decrease in first half
        let lr_25 = scheduler.step(25);
        assert!(lr_25 < lr_0, "LR should decrease in first quarter");

        Ok(())
    }

    #[test]
    fn test_loss_components_creation() {
        let loss = LossComponents {
            physics: 0.1,
            boundary: 0.05,
            initial: 0.02,
            conservation: vec![0.01, 0.015],
            data: 0.0,
            total: 0.185,
            iteration: 42,
        };

        assert!((loss.physics - 0.1).abs() < 1e-10);
        assert!((loss.boundary - 0.05).abs() < 1e-10);
        assert_eq!(loss.conservation.len(), 2);
        assert_eq!(loss.iteration, 42);

        // Test total loss calculation
        let expected_total = loss.physics + loss.boundary + loss.initial +
                           loss.conservation.iter().sum::<f64>() + loss.data;
        assert!((loss.total - expected_total).abs() < 1e-10);
    }

    // Helper function to create test boundary conditions
    fn create_test_boundary_conditions() -> BoundaryConditions {
        // This would be a mock implementation
        // In the real codebase, would use actual BoundaryConditions
        use std::collections::HashMap;

        // Mock implementation that provides the interface needed for testing
        BoundaryConditions::new_for_testing(0.0, 1.0, 0.0, 2.0)
    }

    #[tokio::test]
    async fn test_physics_gradients_structure() {
        use super::super::PhysicsGradients;
        use rtx_autograd::Variable;
        use crate::Tensor;
        use crate::Device;

        // Create mock gradients for testing
        let tensor = Tensor::from_slice(&[1.0, 2.0, 3.0], &[3], Device::cuda(0).unwrap_or(Device::default())).unwrap();
        let variable = Variable::from_tensor(tensor, true);

        let gradients = PhysicsGradients {
            du_dx: variable.clone(),
            du_dt: variable.clone(),
            d2u_dx2: variable.clone(),
            d2u_dt2: variable.clone(),
            d2u_dxdt: variable.clone(),
        };

        // Test that all gradient components are properly initialized
        // In a real test, would verify gradient computations
        assert!(true); // Placeholder assertion for structure test
    }

    #[tokio::test]
    async fn test_training_integration_mock() -> Result<()> {
        // This would be a comprehensive integration test
        // For now, test that the training interface is properly structured

        let config = TrainingConfig {
            learning_rate: 0.001,
            physics_weight: 1.0,
            boundary_weight: 10.0,
            conservation_weights: vec![1.0],
            convergence_tolerance: 1e-6,
            patience: 50,
        };

        let trainer = PINNTrainer::new(config);

        // Test that trainer has all necessary components
        assert!(trainer.config.learning_rate > 0.0);
        assert!(trainer.config.physics_weight > 0.0);
        assert!(trainer.config.boundary_weight > 0.0);
        assert!(!trainer.config.conservation_weights.is_empty());

        Ok(())
    }
}

// Mock BoundaryConditions for testing (would be in actual module)
impl BoundaryConditions {
    pub fn new_for_testing(x_min: f64, x_max: f64, t_min: f64, t_max: f64) -> Self {
        // Mock implementation for testing
        Self {
            spatial_domain: (x_min, x_max),
            temporal_domain: (t_min, t_max),
            // ... other fields would be properly initialized
        }
    }

    pub fn spatial_bounds(&self) -> (f64, f64) {
        self.spatial_domain
    }

    pub fn temporal_bounds(&self) -> (f64, f64) {
        self.temporal_domain
    }

    pub fn get_value_at(&self, _x: f64, _t: f64) -> Option<f64> {
        Some(0.0) // Mock boundary value
    }

    pub fn get_initial_value_at(&self, _x: f64) -> Option<f64> {
        Some(1.0) // Mock initial value
    }
}

// Mock BoundaryConditions structure for testing
#[derive(Debug)]
pub struct BoundaryConditions {
    spatial_domain: (f64, f64),
    temporal_domain: (f64, f64),
}