//! Comprehensive TDD test suite for Physics-Informed Neural Network implementations
//!
//! This module contains failing tests (RED phase) that define the required functionality
//! for complete PINN implementation including model persistence, training enhancements,
//! gradient computations, physics losses, boundary conditions, and adaptive sampling.
#![cfg(feature = "disabled_tests")]

use super::*;
use crate::Tensor;
use crate::error::{Result, ScienceError};
use std::path::Path;

// ================================================================================================
// MODEL WEIGHT PERSISTENCE TESTS - RED PHASE (SHOULD FAIL)
// ================================================================================================

#[tokio::test]
async fn test_model_weight_saving_works() -> Result<()> {
    use tempfile::TempDir;

    let device = crate::Device::cpu();

    let pinn = PINN::builder()
        .device(&device)
        .layers(vec![2, 32, 1])
        .physics_loss(Box::new(HeatEquation::new(0.1)))
        .build()?;

    // Create temporary file for testing
    let temp_dir = TempDir::new()
        .map_err(|e| ScienceError::io_error("Failed to create temp directory", e.to_string()))?;
    let save_path = temp_dir.path().join("test_weights.json");

    // This should now work - GREEN phase
    pinn.save_weights(&save_path).await?;

    // Verify file was created
    assert!(save_path.exists(), "Weight file should be created");

    // Verify file has content
    let file_size = std::fs::metadata(&save_path)
        .map_err(|e| ScienceError::io_error("Failed to read file metadata", e.to_string()))?
        .len();
    assert!(file_size > 0, "Weight file should not be empty");

    // Verify the content is valid JSON
    let content = std::fs::read_to_string(&save_path)
        .map_err(|e| ScienceError::io_error("Failed to read weight file", e.to_string()))?;
    let _parsed: std::collections::HashMap<String, Vec<f32>> = serde_json::from_str(&content)?;

    Ok(())
}

#[tokio::test]
async fn test_model_weight_loading_fails() -> Result<()> {
    let device = crate::Device::cpu();

    let mut pinn = PINN::builder()
        .device(&device)
        .layers(vec![2, 32, 1])
        .physics_loss(Box::new(HeatEquation::new(0.1)))
        .build()?;

    // This method should NOT exist yet - this test should fail to compile
    // Uncomment the line below to see compilation failure:
    // let _ = pinn.load_weights(Path::new("test.safetensors")).await;

    // PINN was successfully created
    Ok(())
}

#[tokio::test]
async fn test_checkpoint_system_fails() -> Result<()> {
    let device = crate::Device::cpu();

    // This should fail because TrainingConfig doesn't have checkpoint fields yet
    // Uncomment to see compilation failure:
    // let config = TrainingConfig {
    //     save_best_model: true,
    //     checkpoint_dir: Some(std::path::PathBuf::from("/tmp")),
    //     checkpoint_frequency: 10,
    //     ..Default::default()
    // };

    let _pinn = PINN::builder()
        .device(&device)
        .layers(vec![2, 32, 1])
        .physics_loss(Box::new(HeatEquation::new(0.1)))
        // .config(config)  // This should fail
        .build()?;

    Ok(())
}

// ================================================================================================
// ADVANCED TRAINING CALLBACK TESTS - RED PHASE (SHOULD FAIL)
// ================================================================================================

#[tokio::test]
async fn test_learning_rate_scheduler_fails() -> Result<()> {
    let device = crate::Device::cpu();

    // This should fail because LearningRateScheduler doesn't exist yet
    // Uncomment to see compilation failure:
    // let scheduler = LearningRateScheduler {
    //     schedule_type: ScheduleType::StepDecay,
    //     initial_lr: 1e-3,
    //     decay_factor: 0.5,
    //     decay_steps: 10,
    // };

    let _pinn = PINN::builder()
        .device(&device)
        .layers(vec![2, 32, 1])
        .physics_loss(Box::new(HeatEquation::new(0.1)))
        .build()?;

    Ok(())
}

#[tokio::test]
async fn test_training_callbacks_fail() -> Result<()> {
    let device = crate::Device::cpu();

    let pinn = PINN::builder()
        .device(&device)
        .layers(vec![2, 32, 1])
        .physics_loss(Box::new(HeatEquation::new(0.1)))
        .build()?;

    let boundary_conditions = BoundaryConditions::new();

    // This should fail because train_with_callbacks doesn't exist yet
    // Uncomment to see compilation failure:
    // let callbacks = vec![];
    // let _ = pinn.train_with_callbacks(boundary_conditions, 10, callbacks).await;

    // Regular training should work
    let mut pinn = pinn;
    pinn.train(boundary_conditions, 10).await?;

    Ok(())
}

// ================================================================================================
// GRADIENT COMPUTATION ENHANCEMENT TESTS - RED PHASE (SHOULD FAIL)
// ================================================================================================

#[tokio::test]
async fn test_higher_order_derivatives_fail() -> Result<()> {
    let device = crate::Device::cpu();

    let pinn = PINN::builder()
        .device(&device)
        .layers(vec![2, 32, 1])
        .physics_loss(Box::new(HeatEquation::new(0.1)))
        .build()?;

    let input_tensor = crate::Tensor::randn(&[10, 2], &device)?;

    // This should fail because compute_higher_order_derivatives doesn't exist yet
    // Uncomment to see compilation failure:
    // let _ = pinn.compute_higher_order_derivatives(&input_tensor, 4).await;

    // Basic derivative computation should work
    let output = pinn.forward(&input_tensor).await?;
    // Note: compute_derivatives is private, so we can't test it directly
    // let _ = pinn.compute_derivatives(&input_tensor, &output).await?;

    Ok(())
}

#[tokio::test]
async fn test_mixed_derivatives_fail() -> Result<()> {
    let device = crate::Device::cpu();

    let pinn = PINN::builder()
        .device(&device)
        .layers(vec![2, 32, 1])
        .physics_loss(Box::new(HeatEquation::new(0.1)))
        .build()?;

    let input_tensor = crate::Tensor::randn(&[5, 2], &device)?;

    // This should fail because compute_mixed_derivatives doesn't exist yet
    // Uncomment to see compilation failure:
    // let _ = pinn.compute_mixed_derivatives(&input_tensor).await;

    Ok(())
}

// ================================================================================================
// ENHANCED PHYSICS LOSS TESTS - RED PHASE (SHOULD FAIL)
// ================================================================================================

#[tokio::test]
async fn test_schrodinger_equation_fails() -> Result<()> {
    let device = crate::Device::cpu();

    // This should fail because Schrodinger doesn't exist yet in our implementation
    // Uncomment to see compilation failure:
    // let schrodinger = Schrodinger::new(1.0, 1.0, std::collections::HashMap::new());

    // For now, test that we can create other PDEs
    let heat_eq = HeatEquation::new(0.1);
    assert_eq!(heat_eq.name(), "Heat Equation");

    Ok(())
}

#[tokio::test]
async fn test_kdv_equation_fails() -> Result<()> {
    // This should fail because KdVEquation implementation is incomplete
    // Uncomment to see issues:
    // let kdv = KdVEquation::new(1.0);
    // assert_eq!(kdv.max_derivative_order(), 3); // KdV needs 3rd derivatives

    Ok(())
}

// ================================================================================================
// BOUNDARY CONDITION ENHANCEMENT TESTS - RED PHASE (SHOULD FAIL)
// ================================================================================================

#[tokio::test]
async fn test_robin_boundary_conditions_fail() -> Result<()> {
    let device = crate::Device::cpu();

    // This should fail because RobinBC support is incomplete
    // Uncomment to see compilation failure:
    // let robin_bc = RobinBC {
    //     boundary_id: "robin_test".to_string(),
    //     geometry: BoundaryGeometry::LineSegment { start: 0.0, end: 1.0 },
    //     alpha: 1.0,
    //     beta: 0.5,
    //     rhs_function: ValueFunction::Constant(2.0),
    //     num_samples: 50,
    //     weight: 5.0,
    // };

    // Basic boundary conditions should work
    let _bc = BoundaryConditions::new();

    Ok(())
}

#[tokio::test]
async fn test_periodic_boundary_conditions_fail() -> Result<()> {
    // This should fail because PeriodicBC implementation is incomplete
    // Uncomment to see compilation failure:
    // let periodic_bc = PeriodicBC {
    //     boundary_pair_id: "periodic_test".to_string(),
    //     left_geometry: BoundaryGeometry::Point { x: 0.0 },
    //     right_geometry: BoundaryGeometry::Point { x: 1.0 },
    //     num_samples: 30,
    //     weight: 8.0,
    // };

    Ok(())
}

// ================================================================================================
// ADAPTIVE SAMPLING TESTS - RED PHASE (SHOULD FAIL)
// ================================================================================================

#[tokio::test]
async fn test_adaptive_sampling_fails() -> Result<()> {
    let device = crate::Device::cpu();

    // This should fail because AdaptiveSamplingConfig doesn't exist yet
    // Uncomment to see compilation failure:
    // let adaptive_config = AdaptiveSamplingConfig {
    //     initial_samples: 100,
    //     max_samples: 1000,
    //     refinement_threshold: 1e-3,
    //     refinement_ratio: 2.0,
    //     resampling_frequency: 10,
    // };

    let _pinn = PINN::builder()
        .device(&device)
        .layers(vec![2, 32, 1])
        .physics_loss(Box::new(HeatEquation::new(0.1)))
        // .adaptive_sampling(adaptive_config)  // This should fail
        .build()?;

    Ok(())
}

#[tokio::test]
async fn test_error_estimator_fails() -> Result<()> {
    // This should fail because ErrorEstimator doesn't exist yet
    // Uncomment to see compilation failure:
    // let error_estimator = ErrorEstimator::new()
    //     .with_reference_solution(ReferenceMethod::FiniteDifference)
    //     .with_tolerance(1e-4);

    Ok(())
}

// ================================================================================================
// MULTI-GPU SUPPORT TESTS - RED PHASE (SHOULD FAIL)
// ================================================================================================

#[cfg(feature = "cuda")]
#[tokio::test]
async fn test_multi_gpu_training_fails() -> Result<()> {
    // This should fail because MultiGpuConfig doesn't exist yet
    // Uncomment to see compilation failure:
    // let multi_gpu_config = MultiGpuConfig {
    //     devices: vec![Device::cuda(0)?, Device::cuda(1)?],
    //     data_parallel: true,
    //     model_parallel: false,
    // };

    Ok(())
}

// ================================================================================================
// CURRENT WORKING TESTS - These should PASS to verify base functionality
// ================================================================================================

#[tokio::test]
async fn test_basic_pinn_creation() -> Result<()> {
    let device = crate::Device::cpu();

    let _pinn = PINN::builder()
        .device(&device)
        .layers(vec![2, 32, 1])
        .physics_loss(Box::new(HeatEquation::new(0.1)))
        .build()?;

    // Basic functionality should work - PINN created successfully
    Ok(())
}

#[tokio::test]
async fn test_basic_training() -> Result<()> {
    let device = crate::Device::cpu();

    let mut pinn = PINN::builder()
        .device(&device)
        .layers(vec![2, 16, 1]) // Smaller network for faster test
        .physics_loss(Box::new(HeatEquation::new(0.1)))
        .build()?;

    let boundary_conditions = BoundaryConditions::new();

    // Basic training should work (even if it's just a placeholder)
    pinn.train(boundary_conditions, 5).await?;

    Ok(())
}

#[tokio::test]
async fn test_prediction() -> Result<()> {
    let device = crate::Device::cpu();

    let pinn = PINN::builder()
        .device(&device)
        .layers(vec![2, 16, 1])
        .physics_loss(Box::new(HeatEquation::new(0.1)))
        .build()?;

    // Create input tensor with shape [2, 2] for 2 samples with 2 features each
    let test_inputs = Tensor::from_slice(&[0.5_f32, 0.5, 0.3, 0.7], &[2, 2], &device)?;
    let predictions = pinn.predict(&test_inputs).await?;

    // Predictions should have shape [2, 1]
    assert_eq!(predictions.shape().dims(), &[2, 1]);

    // Predictions should be finite numbers
    let pred_data: Vec<f32> = predictions.to_vec()?;
    for pred in pred_data {
        assert!(pred.is_finite(), "Predictions should be finite numbers");
    }

    Ok(())
}