/*!
Memory Utilization Benchmarks

Benchmarks measuring memory usage patterns, efficiency, and optimization
across different scenarios. These benchmarks help identify memory bottlenecks
and validate memory management strategies.

## Benchmark Categories

1. **Memory Allocation**: Allocation and deallocation patterns
2. **Memory Fragmentation**: Fragmentation impact on performance
3. **Cache Efficiency**: Memory access pattern optimization
4. **GPU Memory**: GPU memory allocation and transfer patterns
5. **Memory Pools**: Memory pool effectiveness
6. **Large Model Handling**: Memory usage with large models

## Usage

```bash
# Run memory utilization benchmarks
cargo bench --bench memory_utilization

# Run with memory profiling
RUST_LOG=debug cargo bench --bench memory_utilization

# Generate memory usage report
cargo bench --bench memory_utilization -- --memory-report
```
*/

use criterion::{black_box, criterion_group, criterion_main, Criterion, BenchmarkId, Throughput};
use rustytorch_integration_tests::*;
use std::time::Duration;
use tokio::runtime::Runtime;

fn benchmark_memory_allocation_patterns(c: &mut Criterion) {
    let rt = Runtime::new().unwrap();

    let mut group = c.benchmark_group("memory_allocation");
    group.measurement_time(Duration::from_secs(10));

    let allocation_sizes = vec![1024, 4096, 16384, 65536, 262144]; // 1KB to 256KB

    for size in allocation_sizes {
        group.throughput(Throughput::Bytes(size as u64));

        group.bench_with_input(
            BenchmarkId::new("allocate_deallocate", size),
            &size,
            |b, &size| {
                b.to_async(&rt).iter(|| async {
                    benchmark_allocation_cycle(black_box(size)).await
                });
            },
        );

        group.bench_with_input(
            BenchmarkId::new("bulk_allocation", size),
            &size,
            |b, &size| {
                b.to_async(&rt).iter(|| async {
                    benchmark_bulk_allocation(black_box(size)).await
                });
            },
        );
    }

    group.finish();
}

fn benchmark_memory_fragmentation(c: &mut Criterion) {
    let rt = Runtime::new().unwrap();

    let mut group = c.benchmark_group("memory_fragmentation");
    group.measurement_time(Duration::from_secs(15));

    let fragmentation_scenarios = vec![
        ("sequential", false),
        ("fragmented", true),
    ];

    for (scenario, fragmented) in fragmentation_scenarios {
        group.bench_with_input(
            BenchmarkId::new("allocation_pattern", scenario),
            &fragmented,
            |b, &fragmented| {
                b.to_async(&rt).iter(|| async {
                    benchmark_fragmentation_pattern(black_box(fragmented)).await
                });
            },
        );
    }

    group.finish();
}

fn benchmark_cache_efficiency(c: &mut Criterion) {
    let rt = Runtime::new().unwrap();

    let mut group = c.benchmark_group("cache_efficiency");
    group.measurement_time(Duration::from_secs(12));

    let access_patterns = vec![
        ("sequential", AccessPattern::Sequential),
        ("random", AccessPattern::Random),
        ("strided", AccessPattern::Strided),
    ];

    let data_sizes = vec![1024 * 1024, 10 * 1024 * 1024]; // 1MB, 10MB

    for (pattern_name, pattern) in access_patterns {
        for data_size in &data_sizes {
            group.throughput(Throughput::Bytes(*data_size as u64));

            group.bench_with_input(
                BenchmarkId::new(pattern_name, format!("{}MB", data_size / (1024 * 1024))),
                &(*data_size, pattern),
                |b, &(data_size, pattern)| {
                    b.to_async(&rt).iter(|| async {
                        benchmark_memory_access_pattern(black_box(data_size), black_box(pattern)).await
                    });
                },
            );
        }
    }

    group.finish();
}

fn benchmark_gpu_memory_operations(c: &mut Criterion) {
    let rt = Runtime::new().unwrap();

    let mut group = c.benchmark_group("gpu_memory");
    group.measurement_time(Duration::from_secs(20));
    group.sample_size(20); // Fewer samples for GPU operations

    let transfer_sizes = vec![1024 * 1024, 16 * 1024 * 1024, 64 * 1024 * 1024]; // 1MB, 16MB, 64MB

    for size in transfer_sizes {
        group.throughput(Throughput::Bytes(size as u64));

        group.bench_with_input(
            BenchmarkId::new("host_to_device", format!("{}MB", size / (1024 * 1024))),
            &size,
            |b, &size| {
                b.to_async(&rt).iter(|| async {
                    benchmark_host_to_device_transfer(black_box(size)).await
                });
            },
        );

        group.bench_with_input(
            BenchmarkId::new("device_to_host", format!("{}MB", size / (1024 * 1024))),
            &size,
            |b, &size| {
                b.to_async(&rt).iter(|| async {
                    benchmark_device_to_host_transfer(black_box(size)).await
                });
            },
        );

        group.bench_with_input(
            BenchmarkId::new("device_to_device", format!("{}MB", size / (1024 * 1024))),
            &size,
            |b, &size| {
                b.to_async(&rt).iter(|| async {
                    benchmark_device_to_device_transfer(black_box(size)).await
                });
            },
        );
    }

    group.finish();
}

fn benchmark_memory_pools(c: &mut Criterion) {
    let rt = Runtime::new().unwrap();

    let mut group = c.benchmark_group("memory_pools");
    group.measurement_time(Duration::from_secs(15));

    let pool_configurations = vec![
        ("no_pool", PoolConfig::None),
        ("small_pool", PoolConfig::Small),
        ("large_pool", PoolConfig::Large),
        ("adaptive_pool", PoolConfig::Adaptive),
    ];

    for (config_name, pool_config) in pool_configurations {
        group.bench_with_input(
            BenchmarkId::new("pooled_allocation", config_name),
            &pool_config,
            |b, &pool_config| {
                b.to_async(&rt).iter(|| async {
                    benchmark_pooled_allocation(black_box(pool_config)).await
                });
            },
        );
    }

    group.finish();
}

fn benchmark_large_model_memory(c: &mut Criterion) {
    let rt = Runtime::new().unwrap();

    let mut group = c.benchmark_group("large_model_memory");
    group.measurement_time(Duration::from_secs(30));
    group.sample_size(10); // Very few samples for large model tests

    let model_sizes = vec![
        ("small", 100 * 1024 * 1024),    // 100MB
        ("medium", 1024 * 1024 * 1024),  // 1GB
        ("large", 4 * 1024 * 1024 * 1024), // 4GB
    ];

    for (size_name, size_bytes) in model_sizes {
        group.throughput(Throughput::Bytes(size_bytes as u64));

        group.bench_with_input(
            BenchmarkId::new("model_loading", size_name),
            &size_bytes,
            |b, &size_bytes| {
                b.to_async(&rt).iter(|| async {
                    benchmark_large_model_loading(black_box(size_bytes)).await
                });
            },
        );

        group.bench_with_input(
            BenchmarkId::new("model_inference", size_name),
            &size_bytes,
            |b, &size_bytes| {
                b.to_async(&rt).iter(|| async {
                    benchmark_large_model_inference(black_box(size_bytes)).await
                });
            },
        );
    }

    group.finish();
}

fn benchmark_memory_pressure(c: &mut Criterion) {
    let rt = Runtime::new().unwrap();

    let mut group = c.benchmark_group("memory_pressure");
    group.measurement_time(Duration::from_secs(25));

    let pressure_levels = vec![
        ("low", 0.3),     // 30% memory usage
        ("medium", 0.6),  // 60% memory usage
        ("high", 0.9),    // 90% memory usage
    ];

    for (level_name, pressure_ratio) in pressure_levels {
        group.bench_with_input(
            BenchmarkId::new("under_pressure", level_name),
            &pressure_ratio,
            |b, &pressure_ratio| {
                b.to_async(&rt).iter(|| async {
                    benchmark_memory_under_pressure(black_box(pressure_ratio)).await
                });
            },
        );
    }

    group.finish();
}

// Helper types and enums
#[derive(Debug, Clone, Copy)]
enum AccessPattern {
    Sequential,
    Random,
    Strided,
}

#[derive(Debug, Clone, Copy)]
enum PoolConfig {
    None,
    Small,
    Large,
    Adaptive,
}

// Mock benchmark implementations
async fn benchmark_allocation_cycle(size: usize) -> usize {
    // Simulate allocation and deallocation
    let alloc_time = Duration::from_nanos(100 + (size / 1024) as u64 * 10);
    tokio::time::sleep(alloc_time).await;

    let dealloc_time = Duration::from_nanos(50 + (size / 2048) as u64 * 5);
    tokio::time::sleep(dealloc_time).await;

    size
}

async fn benchmark_bulk_allocation(size: usize) -> usize {
    // Simulate bulk allocation of multiple blocks
    let num_blocks = 100;
    let total_size = size * num_blocks;

    let bulk_alloc_time = Duration::from_nanos(500 + (total_size / 1024) as u64 * 5);
    tokio::time::sleep(bulk_alloc_time).await;

    total_size
}

async fn benchmark_fragmentation_pattern(fragmented: bool) -> usize {
    let allocations = if fragmented { 200 } else { 50 };
    let base_time = if fragmented { 2000 } else { 500 }; // Fragmented allocations are slower

    tokio::time::sleep(Duration::from_nanos(base_time + allocations * 10)).await;

    allocations
}

async fn benchmark_memory_access_pattern(data_size: usize, pattern: AccessPattern) -> usize {
    let access_time = match pattern {
        AccessPattern::Sequential => data_size / (1024 * 1024), // Fast sequential access
        AccessPattern::Random => data_size / (512 * 1024),     // Slower random access
        AccessPattern::Strided => data_size / (256 * 1024),    // Slowest strided access
    };

    tokio::time::sleep(Duration::from_micros(access_time as u64)).await;
    data_size
}

async fn benchmark_host_to_device_transfer(size: usize) -> usize {
    // Simulate PCIe transfer bandwidth (~16 GB/s for PCIe 4.0 x16)
    let transfer_time_ns = (size as u64 * 1000) / 16; // nanoseconds
    tokio::time::sleep(Duration::from_nanos(transfer_time_ns)).await;
    size
}

async fn benchmark_device_to_host_transfer(size: usize) -> usize {
    // Similar to host-to-device but slightly slower
    let transfer_time_ns = (size as u64 * 1000) / 14;
    tokio::time::sleep(Duration::from_nanos(transfer_time_ns)).await;
    size
}

async fn benchmark_device_to_device_transfer(size: usize) -> usize {
    // Much faster GPU-to-GPU transfer
    let transfer_time_ns = (size as u64 * 1000) / 400; // ~400 GB/s for high-end GPUs
    tokio::time::sleep(Duration::from_nanos(transfer_time_ns)).await;
    size
}

async fn benchmark_pooled_allocation(pool_config: PoolConfig) -> usize {
    let (alloc_time, efficiency) = match pool_config {
        PoolConfig::None => (1000, 1.0),      // No pooling, baseline
        PoolConfig::Small => (200, 0.8),      // Small pool, fast but less efficient
        PoolConfig::Large => (150, 0.95),     // Large pool, very efficient
        PoolConfig::Adaptive => (100, 0.98),  // Adaptive pool, best of both
    };

    tokio::time::sleep(Duration::from_nanos(alloc_time)).await;

    (1024.0 * efficiency) as usize // Effective allocation size
}

async fn benchmark_large_model_loading(size_bytes: usize) -> usize {
    // Simulate loading time based on disk I/O and memory bandwidth
    let loading_time_ms = (size_bytes / (1024 * 1024)) as u64 * 10; // ~100 MB/s loading
    tokio::time::sleep(Duration::from_millis(loading_time_ms)).await;
    size_bytes
}

async fn benchmark_large_model_inference(size_bytes: usize) -> usize {
    // Simulate inference time scaling with model size
    let inference_base_time = 100; // Base inference time in milliseconds
    let size_factor = (size_bytes / (100 * 1024 * 1024)) as u64; // Per 100MB
    let total_time = inference_base_time + size_factor * 50;

    tokio::time::sleep(Duration::from_millis(total_time)).await;
    size_bytes
}

async fn benchmark_memory_under_pressure(pressure_ratio: f64) -> usize {
    // Simulate performance degradation under memory pressure
    let base_time = 100;
    let pressure_multiplier = if pressure_ratio > 0.8 {
        3.0 // Significant slowdown at high pressure
    } else if pressure_ratio > 0.6 {
        1.5 // Moderate slowdown at medium pressure
    } else {
        1.0 // No slowdown at low pressure
    };

    let total_time = (base_time as f64 * pressure_multiplier) as u64;
    tokio::time::sleep(Duration::from_millis(total_time)).await;

    (1024.0 / pressure_multiplier) as usize // Effective processing amount
}

criterion_group!(
    benches,
    benchmark_memory_allocation_patterns,
    benchmark_memory_fragmentation,
    benchmark_cache_efficiency,
    benchmark_gpu_memory_operations,
    benchmark_memory_pools,
    benchmark_large_model_memory,
    benchmark_memory_pressure
);

criterion_main!(benches);