/*!
Throughput Scaling Benchmarks

Benchmarks measuring throughput scaling characteristics across different
load levels and resource configurations. These benchmarks validate
performance scales appropriately with increased resources.

## Benchmark Categories

1. **Request Throughput**: Requests per second under various loads
2. **Concurrent Processing**: Throughput with concurrent requests
3. **Resource Scaling**: Performance scaling with additional resources
4. **Batch Throughput**: Throughput optimization with batching
5. **Memory Scaling**: Performance vs memory usage trade-offs
6. **GPU Scaling**: Multi-GPU throughput characteristics

## Usage

```bash
# Run throughput scaling benchmarks
cargo bench --bench throughput_scaling

# Run with specific concurrency levels
cargo bench --bench throughput_scaling -- --concurrency 16

# Generate detailed report
cargo bench --bench throughput_scaling -- --verbose
```
*/

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

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

    let mut group = c.benchmark_group("request_throughput");
    group.measurement_time(Duration::from_secs(20));

    // Test different request loads
    let request_counts = vec![100, 500, 1000, 2000];

    for request_count in request_counts {
        group.throughput(Throughput::Elements(request_count as u64));

        group.bench_with_input(
            BenchmarkId::new("sequential_requests", request_count),
            &request_count,
            |b, &request_count| {
                b.to_async(&rt).iter(|| async {
                    benchmark_sequential_requests(black_box(request_count)).await
                });
            },
        );
    }

    group.finish();
}

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

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

    let concurrency_levels = vec![1, 2, 4, 8, 16, 32];
    let requests_per_level = 100;

    for concurrency in concurrency_levels {
        group.throughput(Throughput::Elements(requests_per_level as u64));

        group.bench_with_input(
            BenchmarkId::new("concurrent_requests", concurrency),
            &concurrency,
            |b, &concurrency| {
                b.to_async(&rt).iter(|| async {
                    benchmark_concurrent_requests(black_box(concurrency), requests_per_level).await
                });
            },
        );
    }

    group.finish();
}

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

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

    let resource_levels = vec![
        ("1_cpu", 1),
        ("2_cpu", 2),
        ("4_cpu", 4),
        ("8_cpu", 8),
    ];

    for (label, cpu_count) in resource_levels {
        group.throughput(Throughput::Elements(1000));

        group.bench_with_input(
            BenchmarkId::new("cpu_scaling", label),
            &cpu_count,
            |b, &cpu_count| {
                b.to_async(&rt).iter(|| async {
                    benchmark_cpu_scaling(black_box(cpu_count)).await
                });
            },
        );
    }

    group.finish();
}

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

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

    let batch_configurations = vec![
        (1, 1000),   // No batching
        (8, 1000),   // Small batches
        (32, 1000),  // Medium batches
        (128, 1000), // Large batches
    ];

    for (batch_size, total_items) in batch_configurations {
        group.throughput(Throughput::Elements(total_items as u64));

        group.bench_with_input(
            BenchmarkId::new("batch_processing", format!("batch_{}", batch_size)),
            &(batch_size, total_items),
            |b, &(batch_size, total_items)| {
                b.to_async(&rt).iter(|| async {
                    benchmark_batch_processing_throughput(
                        black_box(batch_size),
                        black_box(total_items)
                    ).await
                });
            },
        );
    }

    group.finish();
}

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

    let mut group = c.benchmark_group("memory_scaling");
    group.measurement_time(Duration::from_secs(20));

    let memory_sizes = vec![
        (1024, "1KB"),
        (1024 * 1024, "1MB"),
        (10 * 1024 * 1024, "10MB"),
        (100 * 1024 * 1024, "100MB"),
    ];

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

        group.bench_with_input(
            BenchmarkId::new("memory_processing", label),
            &size_bytes,
            |b, &size_bytes| {
                b.to_async(&rt).iter(|| async {
                    benchmark_memory_processing(black_box(size_bytes)).await
                });
            },
        );
    }

    group.finish();
}

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

    let mut group = c.benchmark_group("gpu_scaling");
    group.measurement_time(Duration::from_secs(30));
    group.sample_size(10); // Fewer samples for GPU tests

    let gpu_configurations = vec![
        (1, "single_gpu"),
        (2, "dual_gpu"),
        (4, "quad_gpu"),
    ];

    for (gpu_count, label) in gpu_configurations {
        group.throughput(Throughput::Elements(100));

        group.bench_with_input(
            BenchmarkId::new("gpu_processing", label),
            &gpu_count,
            |b, &gpu_count| {
                b.to_async(&rt).iter(|| async {
                    benchmark_gpu_processing(black_box(gpu_count)).await
                });
            },
        );
    }

    group.finish();
}

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

    let mut group = c.benchmark_group("end_to_end_scaling");
    group.measurement_time(Duration::from_secs(30));

    let load_scenarios = vec![
        (10, 1, "light_load"),
        (100, 4, "medium_load"),
        (500, 16, "heavy_load"),
        (1000, 32, "extreme_load"),
    ];

    for (request_count, concurrency, label) in load_scenarios {
        group.throughput(Throughput::Elements(request_count as u64));

        group.bench_with_input(
            BenchmarkId::new("full_pipeline", label),
            &(request_count, concurrency),
            |b, &(request_count, concurrency)| {
                b.to_async(&rt).iter(|| async {
                    benchmark_full_pipeline_scaling(
                        black_box(request_count),
                        black_box(concurrency)
                    ).await
                });
            },
        );
    }

    group.finish();
}

// Mock benchmark implementations
async fn benchmark_sequential_requests(request_count: usize) -> usize {
    let mut processed = 0;
    for _ in 0..request_count {
        // Simulate request processing
        tokio::time::sleep(Duration::from_micros(100)).await;
        processed += 1;
    }
    processed
}

async fn benchmark_concurrent_requests(concurrency: usize, total_requests: usize) -> usize {
    let requests_per_task = total_requests / concurrency;

    let tasks: Vec<_> = (0..concurrency).map(|_| {
        tokio::spawn(async move {
            for _ in 0..requests_per_task {
                // Simulate request processing
                tokio::time::sleep(Duration::from_micros(50)).await;
            }
            requests_per_task
        })
    }).collect();

    let results = join_all(tasks).await;
    results.into_iter().map(|r| r.unwrap()).sum()
}

async fn benchmark_cpu_scaling(cpu_count: usize) -> usize {
    // Simulate CPU-intensive work scaling
    let work_per_cpu = 1000;
    let total_work = cpu_count * work_per_cpu;

    // Simulate parallel processing
    let sleep_time = Duration::from_micros(1000 / cpu_count as u64);
    tokio::time::sleep(sleep_time).await;

    total_work
}

async fn benchmark_batch_processing_throughput(batch_size: usize, total_items: usize) -> usize {
    let num_batches = (total_items + batch_size - 1) / batch_size;

    for _ in 0..num_batches {
        // Simulate batch processing overhead
        let batch_overhead = Duration::from_micros(10 + batch_size as u64 * 5);
        tokio::time::sleep(batch_overhead).await;
    }

    total_items
}

async fn benchmark_memory_processing(size_bytes: usize) -> usize {
    // Simulate memory-intensive processing
    let processing_time = Duration::from_micros((size_bytes / 1024) as u64);
    tokio::time::sleep(processing_time).await;
    size_bytes
}

async fn benchmark_gpu_processing(gpu_count: usize) -> usize {
    // Simulate GPU processing with scaling
    let base_work = 1000;
    let scaled_work = base_work * gpu_count;

    // GPU work scales better than linearly initially, then plateaus
    let efficiency = if gpu_count == 1 {
        1.0
    } else if gpu_count <= 4 {
        gpu_count as f64 * 0.9
    } else {
        4.0 * 0.9 + (gpu_count - 4) as f64 * 0.5
    };

    let processing_time = Duration::from_millis((1000.0 / efficiency) as u64);
    tokio::time::sleep(processing_time).await;

    scaled_work
}

async fn benchmark_full_pipeline_scaling(request_count: usize, concurrency: usize) -> usize {
    // Simulate full pipeline processing under load
    let requests_per_task = (request_count + concurrency - 1) / concurrency;

    let tasks: Vec<_> = (0..concurrency).map(|_| {
        tokio::spawn(async move {
            let mut processed = 0;
            for _ in 0..requests_per_task {
                // Simulate full pipeline: preprocessing, inference, postprocessing
                tokio::time::sleep(Duration::from_micros(200)).await; // preprocessing
                tokio::time::sleep(Duration::from_micros(500)).await; // inference
                tokio::time::sleep(Duration::from_micros(100)).await; // postprocessing
                processed += 1;
            }
            processed
        })
    }).collect();

    let results = join_all(tasks).await;
    results.into_iter().map(|r| r.unwrap()).sum()
}

criterion_group!(
    benches,
    benchmark_request_throughput,
    benchmark_concurrent_processing,
    benchmark_resource_scaling,
    benchmark_batch_throughput,
    benchmark_memory_scaling,
    benchmark_gpu_scaling,
    benchmark_end_to_end_scaling
);

criterion_main!(benches);