/*!
End-to-End Latency Benchmarks

Comprehensive benchmarks measuring end-to-end latency across different
components and scenarios. These benchmarks establish performance baselines
and detect performance regressions.

## Benchmark Categories

1. **Inference Latency**: Single request inference latency
2. **Batch Processing**: Batched inference latency optimization
3. **Model Loading**: Cold start and warm start latency
4. **Pipeline Latency**: Complete ML pipeline execution time
5. **Cross-Component**: Latency across component boundaries
6. **Network Latency**: API and network overhead measurement

## Usage

```bash
# Run all latency benchmarks
cargo bench --bench end_to_end_latency

# Run specific benchmark group
cargo bench --bench end_to_end_latency -- inference

# Generate HTML report
cargo bench --bench end_to_end_latency -- --output-format html
```
*/

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

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

    let mut group = c.benchmark_group("inference_latency");
    group.measurement_time(Duration::from_secs(10));
    group.sample_size(100);

    // Test different input sizes
    let input_sizes = vec![128, 512, 1024, 2048];

    for size in input_sizes {
        group.bench_with_input(
            BenchmarkId::new("single_request", size),
            &size,
            |b, &size| {
                b.to_async(&rt).iter(|| async {
                    let input = vec![0.5f32; size];
                    // Mock inference call
                    benchmark_single_inference(black_box(input)).await
                });
            },
        );
    }

    group.finish();
}

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

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

    let batch_sizes = vec![1, 4, 8, 16, 32];
    let input_size = 512;

    for batch_size in batch_sizes {
        group.bench_with_input(
            BenchmarkId::new("batch_inference", batch_size),
            &batch_size,
            |b, &batch_size| {
                b.to_async(&rt).iter(|| async {
                    let batch = vec![vec![0.5f32; input_size]; batch_size];
                    benchmark_batch_inference(black_box(batch)).await
                });
            },
        );
    }

    group.finish();
}

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

    let mut group = c.benchmark_group("model_loading");
    group.measurement_time(Duration::from_secs(20));
    group.sample_size(10); // Fewer samples for expensive operations

    group.bench_function("cold_start", |b| {
        b.to_async(&rt).iter(|| async {
            benchmark_cold_start().await
        });
    });

    group.bench_function("warm_start", |b| {
        b.to_async(&rt).iter(|| async {
            benchmark_warm_start().await
        });
    });

    group.finish();
}

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

    let mut group = c.benchmark_group("pipeline_latency");
    group.measurement_time(Duration::from_secs(30));
    group.sample_size(20);

    let pipeline_types = vec!["classification", "generation", "embedding"];

    for pipeline_type in pipeline_types {
        group.bench_with_input(
            BenchmarkId::new("end_to_end", pipeline_type),
            &pipeline_type,
            |b, &pipeline_type| {
                b.to_async(&rt).iter(|| async {
                    benchmark_pipeline_execution(black_box(pipeline_type)).await
                });
            },
        );
    }

    group.finish();
}

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

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

    group.bench_function("tensor_to_autograd", |b| {
        b.to_async(&rt).iter(|| async {
            benchmark_tensor_autograd_boundary().await
        });
    });

    group.bench_function("autograd_to_transformers", |b| {
        b.to_async(&rt).iter(|| async {
            benchmark_autograd_transformers_boundary().await
        });
    });

    group.bench_function("transformers_to_serving", |b| {
        b.to_async(&rt).iter(|| async {
            benchmark_transformers_serving_boundary().await
        });
    });

    group.finish();
}

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

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

    group.bench_function("api_overhead", |b| {
        b.to_async(&rt).iter(|| async {
            benchmark_api_overhead().await
        });
    });

    group.bench_function("serialization", |b| {
        b.to_async(&rt).iter(|| async {
            let data = vec![0.5f32; 1024];
            benchmark_serialization_overhead(black_box(data)).await
        });
    });

    group.finish();
}

// Mock benchmark functions - would be replaced with actual implementations
async fn benchmark_single_inference(input: Vec<f32>) -> Vec<f32> {
    // Simulate inference computation
    tokio::time::sleep(Duration::from_micros(100)).await;
    vec![0.1, 0.2, 0.3] // Mock output
}

async fn benchmark_batch_inference(batch: Vec<Vec<f32>>) -> Vec<Vec<f32>> {
    // Simulate batch inference
    let batch_size = batch.len();
    tokio::time::sleep(Duration::from_micros(50 + batch_size as u64 * 20)).await;
    vec![vec![0.1, 0.2, 0.3]; batch_size]
}

async fn benchmark_cold_start() -> String {
    // Simulate model loading from disk
    tokio::time::sleep(Duration::from_millis(500)).await;
    "model_loaded".to_string()
}

async fn benchmark_warm_start() -> String {
    // Simulate model already in memory
    tokio::time::sleep(Duration::from_millis(50)).await;
    "model_ready".to_string()
}

async fn benchmark_pipeline_execution(pipeline_type: &str) -> String {
    // Simulate complete pipeline execution
    let base_latency = match pipeline_type {
        "classification" => 200,
        "generation" => 1000,
        "embedding" => 100,
        _ => 300,
    };

    tokio::time::sleep(Duration::from_millis(base_latency)).await;
    format!("{}_result", pipeline_type)
}

async fn benchmark_tensor_autograd_boundary() -> String {
    // Simulate tensor to autograd operation
    tokio::time::sleep(Duration::from_micros(50)).await;
    "gradient_computed".to_string()
}

async fn benchmark_autograd_transformers_boundary() -> String {
    // Simulate autograd to transformers operation
    tokio::time::sleep(Duration::from_micros(75)).await;
    "forward_pass_complete".to_string()
}

async fn benchmark_transformers_serving_boundary() -> String {
    // Simulate transformers to serving operation
    tokio::time::sleep(Duration::from_micros(25)).await;
    "response_serialized".to_string()
}

async fn benchmark_api_overhead() -> String {
    // Simulate API call overhead
    tokio::time::sleep(Duration::from_micros(10)).await;
    "api_response".to_string()
}

async fn benchmark_serialization_overhead(data: Vec<f32>) -> Vec<u8> {
    // Simulate serialization overhead
    tokio::time::sleep(Duration::from_micros(5 + data.len() as u64 / 100)).await;
    vec![0; data.len() * 4] // Mock serialized data
}

criterion_group!(
    benches,
    benchmark_inference_latency,
    benchmark_batch_processing,
    benchmark_model_loading,
    benchmark_pipeline_latency,
    benchmark_cross_component_latency,
    benchmark_network_latency
);

criterion_main!(benches);