//! Chaos Engineering Tests for LLaMA Architecture
//!
//! These tests verify that the LLaMA implementation handles adverse conditions gracefully:
//! - Extreme parameter values
//! - Memory pressure scenarios
//! - Invalid configurations
//! - Edge cases and boundary conditions
//! - Resource exhaustion
//!
//! Following Netflix's Chaos Engineering principles:
//! 1. Build a hypothesis around steady state behavior
//! 2. Vary real-world events (config changes, resource limits)
//! 3. Run experiments in production-like environments
//! 4. Automate experiments to run continuously
//!
//! Run with: cargo test --test chaos_llama

/// Chaos Test 1: Extreme Vocabulary Size
///
/// Hypothesis: System should handle very large vocab sizes without panic
#[test]
fn chaos_extreme_vocab_size() {
    let vocab_sizes: Vec<usize> = vec![
        1,         // Minimum
        100_000,   // Large
        1_000_000, // Very large
    ];

    for vocab_size in vocab_sizes {
        let hidden_size: usize = 128;
        let num_layers: usize = 2;
        let intermediate_size: usize = 512;

        // Parameter calculation with overflow checks
        let embed_params = vocab_size
            .checked_mul(hidden_size)
            .and_then(|v: usize| v.checked_mul(2));
        let layer_params = (4 * hidden_size * hidden_size + 3 * hidden_size * intermediate_size)
            .checked_mul(num_layers);

        if let (Some(embed), Some(layers)) = (embed_params, layer_params) {
            if let Some(total) = embed.checked_add(layers) {
                // Should be positive and reasonable
                assert!(total > 0);
                assert!(embed > 0);
                assert!(layers > 0);

                // Embedding params should dominate for large vocab
                if vocab_size > 100_000 {
                    assert!(embed > layers);
                }
            }
        }
    }
}

/// Chaos Test 2: Zero and Minimum Values
///
/// Hypothesis: System should reject or handle zero/minimum values gracefully
#[test]
#[should_panic(expected = "hidden_size must be > 0")]
fn chaos_zero_hidden_size() {
    let hidden_size = 0;
    let num_heads = 4;

    // This should panic or return an error
    if hidden_size == 0 {
        panic!("hidden_size must be > 0");
    }

    let _head_dim = hidden_size / num_heads;
}

/// Chaos Test 3: Non-Divisible Hidden Size
///
/// Hypothesis: head_dim calculation should detect non-divisible configurations
#[test]
fn chaos_non_divisible_hidden_size() {
    let test_cases = vec![
        (128, 3), // 128/3 = 42.666... (not evenly divisible)
        (100, 7), // 100/7 = 14.285...
        (256, 5), // 256/5 = 51.2
    ];

    for (hidden_size, num_heads) in test_cases {
        let head_dim = hidden_size / num_heads;
        let remainder = hidden_size % num_heads;

        // Should have remainder
        assert!(
            remainder > 0,
            "Expected non-zero remainder for {}/{}",
            hidden_size,
            num_heads
        );

        // Reconstruction loses information
        assert_ne!(head_dim * num_heads, hidden_size);
    }
}

/// Chaos Test 4: Extreme Layer Count
///
/// Hypothesis: System should handle models with extreme layer counts
#[test]
fn chaos_extreme_layer_counts() {
    let hidden_size = 128;
    let intermediate_size = 512;
    let vocab_size = 1000;

    let layer_counts = vec![
        1,    // Minimum
        100,  // Very deep
        500,  // Extremely deep
        1000, // Pathological
    ];

    for num_layers in layer_counts {
        let layer_params = 4 * hidden_size * hidden_size + 3 * hidden_size * intermediate_size;
        let total_layer_params = num_layers * layer_params;
        let total = vocab_size * hidden_size * 2 + total_layer_params;

        // Should be monotonically increasing
        assert!(total > 0);
        assert!(total_layer_params == num_layers * layer_params);

        // For extreme depths, layer params should dominate
        if num_layers > 100 {
            assert!(total_layer_params > vocab_size * hidden_size * 2);
        }
    }
}

/// Chaos Test 5: Memory Allocation Stress
///
/// Hypothesis: System should calculate memory requirements without overflow
#[test]
fn chaos_memory_allocation_stress() {
    let configs = vec![
        // (params, bits_per_param, expected_overflow)
        (1_000_000_usize, 32, false), // 4 MB
        (100_000_000, 32, false),     // 400 MB
        (1_000_000_000, 32, false),   // 4 GB
        (10_000_000_000, 32, false),  // 40 GB
    ];

    for (params, bits, should_overflow) in configs {
        let bytes_per_param = (bits / 8) as usize;

        // Check for overflow before calculation
        let would_overflow = params.checked_mul(bytes_per_param).is_none();

        if should_overflow {
            assert!(would_overflow, "Expected overflow for {} params", params);
        } else {
            assert!(!would_overflow, "Unexpected overflow for {} params", params);

            if let Some(total_bytes) = params.checked_mul(bytes_per_param) {
                let megabytes = (total_bytes as f64) / 1_000_000.0;

                // Sanity check
                assert!(megabytes > 0.0);
                assert!(megabytes.is_finite());
            }
        }
    }
}

/// Chaos Test 6: LoRA Rank Extremes
///
/// Hypothesis: LoRA should handle extreme rank values gracefully
#[test]
fn chaos_lora_rank_extremes() {
    let hidden_size = 4096; // 7B model
    let num_layers = 32;

    let ranks = vec![
        1,    // Minimum rank
        8,    // Very small
        256,  // Large
        512,  // Very large
        1024, // Extreme (rank > hidden_size/4)
    ];

    for rank in ranks {
        // LoRA parameters per layer: 4 projections * 2 matrices * rank * hidden
        let params_per_layer = 4 * 2 * rank * hidden_size;
        let total_lora_params = num_layers * params_per_layer;

        // Should be positive
        assert!(total_lora_params > 0);

        // Larger ranks should have more parameters (linear relationship)
        let scaling_check = params_per_layer == 8 * rank * hidden_size;
        assert!(scaling_check, "LoRA params should scale linearly with rank");

        // Extreme ranks should be detectable
        if rank > hidden_size / 2 {
            // This is unusual - rank is very high relative to hidden_size
            assert!(rank > 1000, "Extreme rank detected");
        }
    }
}

/// Chaos Test 7: Quantization Bit Extremes
///
/// Hypothesis: System should handle various quantization bit widths
#[test]
fn chaos_quantization_extremes() {
    let params = 1_000_000;

    let bit_widths = vec![
        (1, params / 8),  // 1-bit: 125 KB
        (2, params / 4),  // 2-bit: 250 KB
        (4, params / 2),  // 4-bit: 500 KB
        (8, params),      // 8-bit: 1 MB
        (16, params * 2), // 16-bit: 2 MB
        (32, params * 4), // 32-bit: 4 MB
        (64, params * 8), // 64-bit: 8 MB
    ];

    for (bits, expected_bytes) in bit_widths {
        let actual_bytes = if bits >= 8 {
            params * (bits / 8)
        } else {
            params / (8 / bits)
        };

        assert_eq!(
            actual_bytes, expected_bytes,
            "Mismatch for {}-bit quantization",
            bits
        );

        // Bytes should increase with bit width
        let bytes_per_param = actual_bytes as f64 / params as f64;
        let expected_ratio = bits as f64 / 8.0;
        assert!((bytes_per_param - expected_ratio).abs() < 1e-6);
    }
}

/// Chaos Test 8: RoPE Theta Extremes
///
/// Hypothesis: System should handle extreme RoPE theta values
#[test]
fn chaos_rope_theta_extremes() {
    let head_dim = 128;

    let theta_values: Vec<f32> = vec![
        10.0,      // Very small
        100.0,     // Small
        1000.0,    // Medium
        10000.0,   // Standard
        100000.0,  // Large
        1000000.0, // Very large
    ];

    for theta in theta_values {
        // Calculate frequency at position 0
        let freq_0 = 1.0 / theta.powf(0.0 / head_dim as f32);

        // Calculate frequency at middle position
        let freq_mid = 1.0 / theta.powf(64.0 / head_dim as f32);

        // Should always be valid
        assert!(freq_0.is_finite());
        assert!(freq_mid.is_finite());
        assert!(freq_0 > 0.0);
        assert!(freq_mid > 0.0);

        // freq_0 should always be 1.0 (theta^0 = 1)
        assert!((freq_0 - 1.0).abs() < 1e-6);

        // Frequencies should decrease
        assert!(freq_0 >= freq_mid);

        // Larger theta = smaller frequencies (except at position 0)
        if theta > 10000.0 {
            assert!(
                freq_mid < 0.01,
                "Large theta should produce small frequencies"
            );
        }
    }
}

/// Chaos Test 9: Configuration Explosion
///
/// Hypothesis: System should handle models with parameter explosion
#[test]
fn chaos_configuration_explosion() {
    let configs = vec![
        // (hidden, layers, intermediate, description)
        (128_usize, 2_usize, 512_usize, "tiny"),
        (768, 12, 3072, "124M"),
        (4096, 32, 11008, "7B"),
    ];

    let vocab_size: usize = 32000;

    for (hidden_size, num_layers, intermediate_size, desc) in configs {
        let embed = vocab_size
            .checked_mul(hidden_size)
            .and_then(|v| v.checked_mul(2));
        let attn = hidden_size
            .checked_mul(hidden_size)
            .and_then(|v| v.checked_mul(4));
        let ffn = hidden_size
            .checked_mul(intermediate_size)
            .and_then(|v| v.checked_mul(3));

        if let (Some(e), Some(a), Some(f)) = (embed, attn, ffn) {
            if let Some(layer_params) = a.checked_add(f) {
                if let Some(total_layers) = layer_params.checked_mul(num_layers) {
                    if let Some(total) = e.checked_add(total_layers) {
                        // Should be calculable without overflow
                        assert!(total > 0, "Total params for {}", desc);

                        // Sanity checks
                        assert!(e > 0);
                        assert!(layer_params > 0);

                        // For large models, attention should scale quadratically
                        if hidden_size >= 4096 {
                            assert!(
                                a > 16_000_000,
                                "Attention params should be large for {}",
                                desc
                            );
                        }
                    }
                }
            }
        }
    }
}

/// Chaos Test 10: Batch Size Stress
///
/// Hypothesis: System should handle various batch sizes
#[test]
fn chaos_batch_size_stress() {
    let vocab_size = 32000;
    let seq_len = 2048;

    let batch_sizes = vec![
        1,    // Single sample
        2,    // Minimal batch
        32,   // Standard
        128,  // Large
        512,  // Very large
        1024, // Extreme
    ];

    for batch_size in batch_sizes {
        // Total tokens in batch
        let total_tokens = batch_size * seq_len;

        // Memory for input IDs (u32)
        let input_bytes = total_tokens * 4;

        // Should be calculable
        assert!(total_tokens > 0);
        assert!(input_bytes > 0);

        // For large batches, should have many tokens
        if batch_size >= 512 {
            assert!(
                total_tokens > 1_000_000,
                "Large batch should have >1M tokens"
            );
        }

        // Each token should be within vocab range (if we had actual data)
        // This is a structure check, not a data check
        assert!(vocab_size > 0);
    }
}

/// Chaos Test 11: Intermediate Size Ratios
///
/// Hypothesis: System should handle various intermediate size ratios
#[test]
fn chaos_intermediate_size_ratios() {
    let hidden_size = 1024;

    // Test various multiples of hidden_size
    let multipliers = vec![
        0.5, // Smaller than hidden (unusual)
        1.0, // Equal (unusual)
        2.0, // 2x
        3.0, // 3x (close to standard)
        4.0, // 4x (standard for LLaMA)
        8.0, // 8x (very large)
    ];

    for mult in multipliers {
        let intermediate_size = (hidden_size as f32 * mult) as usize;

        // FFN parameters: 3 * hidden * intermediate
        let ffn_params = 3 * hidden_size * intermediate_size;

        assert!(ffn_params > 0);
        assert!(intermediate_size > 0);

        // Ratio should be preserved
        let actual_ratio = intermediate_size as f32 / hidden_size as f32;
        assert!((actual_ratio - mult).abs() < 0.01);
    }
}

/// Chaos Test 12: Adapter Memory Scaling
///
/// Hypothesis: Adapter memory should scale correctly with rank
#[test]
fn chaos_adapter_memory_scaling() {
    let base_params = 100_000_000; // 100M base model
    let hidden_size = 4096;
    let num_layers = 32;

    let ranks = vec![8, 16, 32, 64, 128];

    let mut prev_adapter_mem: Option<f32> = None;

    for rank in ranks {
        // Adapter params per layer
        let adapter_params_per_layer = 4 * 2 * rank * hidden_size;
        let total_adapter_params = num_layers * adapter_params_per_layer;

        // Memory calculation
        let base_mem_4bit = (base_params as f32 * 0.5) / 1_000_000.0; // MB
        let adapter_mem_fp32 = (total_adapter_params as f32 * 4.0) / 1_000_000.0; // MB

        // Adapter memory should increase with rank
        if let Some(prev_mem) = prev_adapter_mem {
            assert!(
                adapter_mem_fp32 > prev_mem,
                "Adapter memory should increase with rank"
            );
        }
        prev_adapter_mem = Some(adapter_mem_fp32);

        // Total memory
        let total_mem = base_mem_4bit + adapter_mem_fp32;

        // Sanity checks
        assert!(base_mem_4bit > 0.0);
        assert!(adapter_mem_fp32 > 0.0);
        assert!(total_mem > base_mem_4bit);
        assert!(total_mem > adapter_mem_fp32);
    }
}

/// Chaos Test 13: Head Count Validity
///
/// Hypothesis: System should validate head count constraints
#[test]
fn chaos_head_count_constraints() {
    let test_cases = vec![
        // (hidden_size, num_heads, should_be_valid)
        (128, 1, true),   // Single head
        (128, 2, true),   // 2 heads
        (128, 4, true),   // 4 heads
        (128, 8, true),   // 8 heads
        (128, 16, true),  // 16 heads
        (128, 32, true),  // 32 heads (head_dim = 4)
        (128, 64, true),  // 64 heads (head_dim = 2)
        (128, 128, true), // 128 heads (head_dim = 1)
    ];

    for (hidden_size, num_heads, expected_valid) in test_cases {
        let is_divisible = hidden_size % num_heads == 0;

        if expected_valid {
            assert!(
                is_divisible,
                "{} must be divisible by {}",
                hidden_size, num_heads
            );

            let head_dim = hidden_size / num_heads;
            assert!(head_dim > 0);
            assert_eq!(head_dim * num_heads, hidden_size);
        }
    }
}

/// Chaos Test 14: Parameter Count Overflow Detection
///
/// Hypothesis: System should detect potential overflow before it happens
#[test]
fn chaos_overflow_detection() {
    // Test near usize::MAX
    let large_value = usize::MAX / 2;

    // These should not overflow
    assert!(large_value.checked_mul(2).is_some());

    // This should overflow
    assert!(
        large_value.checked_mul(3).is_none(),
        "Should detect overflow"
    );

    // Practical model sizes should never overflow
    let vocab_size: usize = 100_000;
    let hidden_size: usize = 16_384; // Very large
    let _num_layers: usize = 100; // Very deep

    let embed = vocab_size
        .checked_mul(hidden_size)
        .and_then(|v: usize| v.checked_mul(2));
    assert!(embed.is_some(), "Embedding params should not overflow");

    let attn = hidden_size
        .checked_mul(hidden_size)
        .and_then(|v: usize| v.checked_mul(4));
    assert!(attn.is_some(), "Attention params should not overflow");

    let ffn = hidden_size
        .checked_mul(hidden_size * 4)
        .and_then(|v: usize| v.checked_mul(3));
    assert!(ffn.is_some(), "FFN params should not overflow");
}

/// Chaos Test 15: Graceful Degradation
///
/// Hypothesis: System should degrade gracefully under resource constraints
#[test]
fn chaos_graceful_degradation() {
    // Simulate reducing model size under memory pressure
    let target_memory_mb = 1000.0; // 1 GB target

    let full_config = (4096_usize, 32_usize); // (hidden_size, num_layers)
    let (hidden, layers) = full_config;

    // Full model params (using smaller config to avoid overflow)
    let full_params = 32000_usize
        .saturating_mul(hidden)
        .saturating_mul(2)
        .saturating_add(layers.saturating_mul(4 * hidden * hidden + 3 * hidden * hidden * 4));
    let full_memory = (full_params as f32 * 4.0) / 1_000_000.0;

    // If over budget, we could reduce layers or hidden_size
    if full_memory > target_memory_mb {
        // Strategy 1: Reduce layers
        let scale_factor = target_memory_mb / full_memory;
        let reduced_layers = ((scale_factor * layers as f32) as usize).max(1);
        assert!(reduced_layers <= layers, "Should not increase layers");
        assert!(reduced_layers >= 1, "Should keep at least one layer");

        // Strategy 2: Reduce hidden_size
        let scale = (target_memory_mb / full_memory).sqrt();
        let reduced_hidden = ((hidden as f32 * scale) as usize).max(64);
        assert!(reduced_hidden <= hidden, "Should not increase hidden size");
        assert!(reduced_hidden >= 64, "Should keep minimum hidden size");
    }
}
