mecab-ko 0.7.2

//! Performance regression tests
//!
//! This module tests performance characteristics and prevents regressions:
//! - Tokenization throughput
//! - Dictionary lookup speed
//! - Memory usage
//! - Scaling with input size

#![allow(clippy::expect_used, clippy::unwrap_used, clippy::unreadable_literal)]

mod common;

/// Performance baseline thresholds (in microseconds)
#[allow(dead_code)]
const BASELINE_SIMPLE_SENTENCE_US: f64 = 100.0;
#[allow(dead_code)]
const BASELINE_COMPLEX_SENTENCE_US: f64 = 500.0;
#[allow(dead_code)]
const BASELINE_DICT_LOOKUP_US: f64 = 10.0;

/// Test tokenization performance for simple sentences
#[test]
fn test_tokenize_simple_performance() {
    // TODO: Implement once tokenizer is available
    // let tokenizer = Tokenizer::new().expect("Failed to create tokenizer");
    // let text = "안녕하세요";
    //
    // let result = perf::measure("Simple tokenization", 1000, || {
    //     tokenizer.tokenize(text);
    // });
    //
    // println!("{}", result.format());
    // perf::assert_performance(&result, BASELINE_SIMPLE_SENTENCE_US);

    println!("Simple sentence performance test (placeholder)");
}

/// Test tokenization performance for complex sentences
#[test]
fn test_tokenize_complex_performance() {
    // TODO: Implement once tokenizer is available
    // let tokenizer = Tokenizer::new().expect("Failed to create tokenizer");
    // let text = "서울대학교에서 인공지능과 머신러닝을 연구하고 있습니다.";
    //
    // let result = perf::measure("Complex tokenization", 1000, || {
    //     tokenizer.tokenize(text);
    // });
    //
    // println!("{}", result.format());
    // perf::assert_performance(&result, BASELINE_COMPLEX_SENTENCE_US);

    println!("Complex sentence performance test (placeholder)");
}

/// Test dictionary lookup performance
#[test]
fn test_dict_lookup_performance() {
    // TODO: Implement once dictionary is available
    // let dict = load_test_dictionary();
    // let word = "안녕";
    //
    // let result = perf::measure("Dictionary lookup", 10000, || {
    //     dict.lookup(word);
    // });
    //
    // println!("{}", result.format());
    // perf::assert_performance(&result, BASELINE_DICT_LOOKUP_US);

    println!("Dictionary lookup performance test (placeholder)");
}

/// Test performance scaling with input length
#[test]
fn test_performance_scaling() {
    // TODO: Implement once tokenizer is available
    // let tokenizer = Tokenizer::new().expect("Failed to create tokenizer");
    //
    // let sizes = vec![10, 50, 100, 500, 1000];
    // let base_text = "안녕하세요. ";
    //
    // for size in sizes {
    //     let text: String = base_text.repeat(size);
    //     let result = perf::measure(&format!("Tokenize {} chars", text.len()), 100, || {
    //         tokenizer.tokenize(&text);
    //     });
    //
    //     println!("{}", result.format());
    // }

    println!("Performance scaling test (placeholder)");
}

/// Test memory usage during tokenization
#[test]
fn test_memory_usage() {
    // TODO: Implement memory usage testing
    // This might require additional tools like jemalloc or valgrind
    println!("Memory usage test (placeholder)");
}

/// Test throughput (tokens per second)
#[test]
fn test_throughput() {
    // use std::time::Instant;

    // TODO: Implement once tokenizer is available
    // let tokenizer = Tokenizer::new().expect("Failed to create tokenizer");
    // let sentences = SampleTextGenerator::basic_sentences();
    //
    // let start = Instant::now();
    // let mut total_tokens = 0;
    //
    // for _ in 0..100 {
    //     for sentence in &sentences {
    //         let tokens = tokenizer.tokenize(sentence);
    //         total_tokens += tokens.len();
    //     }
    // }
    //
    // let duration = start.elapsed();
    // let tokens_per_sec = total_tokens as f64 / duration.as_secs_f64();
    //
    // println!("Throughput: {:.0} tokens/sec", tokens_per_sec);
    // assert!(tokens_per_sec > 10000.0, "Throughput should be > 10k tokens/sec");

    println!("Throughput test (placeholder)");
}

/// Test cold start performance (first run)
#[test]
fn test_cold_start_performance() {
    // use std::time::Instant;

    // TODO: Implement once tokenizer is available
    // let start = Instant::now();
    // let tokenizer = Tokenizer::new().expect("Failed to create tokenizer");
    // let init_time = start.elapsed();
    //
    // println!("Cold start time: {:.2}ms", init_time.as_secs_f64() * 1000.0);
    // assert!(init_time.as_secs() < 1, "Cold start should be < 1 second");

    println!("Cold start performance test (placeholder)");
}

/// Test warm performance (after JIT warmup)
#[test]
fn test_warm_performance() {
    // TODO: Implement once tokenizer is available
    // let tokenizer = Tokenizer::new().expect("Failed to create tokenizer");
    // let text = "안녕하세요";
    //
    // // Warmup
    // for _ in 0..100 {
    //     tokenizer.tokenize(text);
    // }
    //
    // // Measure warm performance
    // let result = perf::measure("Warm tokenization", 1000, || {
    //     tokenizer.tokenize(text);
    // });
    //
    // println!("{}", result.format());

    println!("Warm performance test (placeholder)");
}

/// Test parallel processing performance
#[test]
fn test_parallel_performance() {
    // use std::sync::Arc;
    // use std::thread;

    // TODO: Implement once parallel processing is supported
    // let tokenizer = Arc::new(Tokenizer::new().expect("Failed to create tokenizer"));
    // let sentences = Arc::new(SampleTextGenerator::basic_sentences());
    //
    // let mut handles = vec![];
    // let num_threads = 4;
    //
    // let start = Instant::now();
    //
    // for _ in 0..num_threads {
    //     let tokenizer = Arc::clone(&tokenizer);
    //     let sentences = Arc::clone(&sentences);
    //
    //     let handle = thread::spawn(move || {
    //         for _ in 0..100 {
    //             for sentence in sentences.iter() {
    //                 tokenizer.tokenize(sentence);
    //             }
    //         }
    //     });
    //     handles.push(handle);
    // }
    //
    // for handle in handles {
    //     handle.join().expect("Thread panicked");
    // }
    //
    // let duration = start.elapsed();
    // println!("Parallel processing time: {:.2}ms", duration.as_secs_f64() * 1000.0);

    println!("Parallel performance test (placeholder)");
}

/// Test batch processing performance
#[test]
fn test_batch_performance() {
    // TODO: Implement once batch processing is available
    // let tokenizer = Tokenizer::new().expect("Failed to create tokenizer");
    // let sentences = SampleTextGenerator::complex_sentences();
    //
    // // Single processing
    // let single_result = perf::measure("Single processing", 100, || {
    //     for sentence in &sentences {
    //         tokenizer.tokenize(sentence);
    //     }
    // });
    //
    // // Batch processing
    // let batch_result = perf::measure("Batch processing", 100, || {
    //     tokenizer.tokenize_batch(&sentences);
    // });
    //
    // println!("Single: {}", single_result.format());
    // println!("Batch: {}", batch_result.format());
    //
    // // Batch should be faster
    // assert!(batch_result.avg_per_iter < single_result.avg_per_iter);

    println!("Batch performance test (placeholder)");
}

/// Test performance with different text types
#[test]
fn test_text_type_performance() {
    // TODO: Implement once tokenizer is available
    // let tokenizer = Tokenizer::new().expect("Failed to create tokenizer");
    //
    // let text_types = vec![
    //     ("basic", SampleTextGenerator::basic_sentences()),
    //     ("complex", SampleTextGenerator::complex_sentences()),
    //     ("technical", SampleTextGenerator::technical_sentences()),
    // ];
    //
    // for (name, sentences) in text_types {
    //     let result = perf::measure(&format!("{} text", name), 100, || {
    //         for sentence in &sentences {
    //             tokenizer.tokenize(sentence);
    //         }
    //     });
    //     println!("{}", result.format());
    // }

    println!("Text type performance test (placeholder)");
}

/// Test performance regression detection
#[test]
fn test_performance_regression() {
    // TODO: Implement baseline comparison
    // Load previous baseline measurements
    // Run current measurements
    // Compare and fail if regression > threshold (e.g., 10%)

    println!("Performance regression test (placeholder)");
}

/// Benchmark different dictionary implementations
#[test]
fn bench_dictionary_implementations() {
    // TODO: Compare performance of:
    // - Dense matrix
    // - Sparse matrix
    // - Memory-mapped matrix
    // - FST-based trie
    // - Double-array trie

    println!("Dictionary implementation benchmark (placeholder)");
}

/// Benchmark different lattice algorithms
#[test]
fn bench_lattice_algorithms() {
    // TODO: Compare performance of:
    // - Viterbi search
    // - N-best search
    // - Beam search

    println!("Lattice algorithm benchmark (placeholder)");
}

/// Test performance with large documents
#[test]
fn test_large_document_performance() {
    // TODO: Implement once tokenizer is available
    // let tokenizer = Tokenizer::new().expect("Failed to create tokenizer");
    //
    // // Generate large document (10KB, 100KB, 1MB)
    // let sizes = vec![10_000, 100_000, 1_000_000];
    //
    // for size in sizes {
    //     let text = "안녕하세요. ".repeat(size / 12); // Approximate byte count
    //     let result = perf::measure(&format!("Document ~{}KB", size / 1000), 10, || {
    //         tokenizer.tokenize(&text);
    //     });
    //     println!("{}", result.format());
    // }

    println!("Large document performance test (placeholder)");
}

/// Test performance with worst-case inputs
#[test]
fn test_worst_case_performance() {
    // TODO: Implement worst-case scenarios:
    // - Very long words
    // - Many unknown words
    // - Mixed scripts
    // - Dense punctuation

    println!("Worst-case performance test (placeholder)");
}

#[cfg(test)]
mod micro_benchmarks {
    use crate::common::perf;

    /// Micro-benchmark: character classification
    #[test]
    fn bench_char_classification() {
        use mecab_ko_hangul::is_hangul;

        let result = perf::measure("Hangul detection", 100000, || {
            let _ = is_hangul('가');
        });

        println!("{}", result.format());
        // Should be extremely fast (< 1μs) - threshold is relaxed for CI/test-harness overhead
        perf::assert_performance(&result, 1.0);
    }

    /// Micro-benchmark: jamo decomposition
    #[test]
    fn bench_jamo_decomposition() {
        use mecab_ko_hangul::decompose;

        let result = perf::measure("Jamo decomposition", 100000, || {
            let _ = decompose('한');
        });

        println!("{}", result.format());
        // Should be very fast (< 5μs) - threshold is relaxed for CI/test-harness overhead
        perf::assert_performance(&result, 5.0);
    }

    /// Micro-benchmark: jamo composition
    #[test]
    fn bench_jamo_composition() {
        use mecab_ko_hangul::compose;

        let result = perf::measure("Jamo composition", 100000, || {
            let _ = compose('ㅎ', 'ㅏ', Some('ㄴ'));
        });

        println!("{}", result.format());
        // Should be very fast (< 5μs) - threshold is relaxed for CI/test-harness overhead
        perf::assert_performance(&result, 5.0);
    }
}

#[cfg(test)]
mod memory_benchmarks {

    /// Test memory allocation patterns
    #[test]
    fn test_allocation_patterns() {
        // TODO: Profile allocation patterns
        // - Number of allocations
        // - Peak memory usage
        // - Memory fragmentation
        println!("Allocation patterns test (placeholder)");
    }

    /// Test memory leaks
    #[test]
    fn test_memory_leaks() {
        // TODO: Run with valgrind or AddressSanitizer
        // to detect memory leaks
        println!("Memory leak test (placeholder)");
    }
}