//! Y6: APR Decode Benchmark Tests (EXTREME TDD - RED Phase)
//!
//! Per Section Y of the spec, APR decode must achieve >=50 tok/s on CPU.
//! These tests define Popperian falsification conditions for Y6.
//!
//! FALSIFICATION: APR < 50 tok/s when GGUF >= 50 tok/s

// ============================================================================
// Y6.1: AprBenchmarkRunner Exists
// ============================================================================

/// Y6.1a: AprBenchmarkRunner struct exists with correct interface
/// FALSIFICATION: Struct missing or wrong methods
#[test]
fn y6_1a_apr_benchmark_runner_exists() {
    use realizar::apr_transformer::{AprBenchmarkRunner, AprTransformer, AprTransformerConfig};

    let config = AprTransformerConfig {
        hidden_dim: 64,
        num_layers: 1,
        num_heads: 4,
        num_kv_heads: 4,
        vocab_size: 100,
        intermediate_dim: 128,
        context_length: 32,
        ..Default::default()
    };

    let transformer = AprTransformer::new(config);

    // Must be constructible from transformer
    let runner = AprBenchmarkRunner::new(transformer);

    // Must have benchmark methods
    assert!(
        runner.warmup_iterations() >= 1,
        "Should have warmup iterations"
    );
    assert!(
        runner.measure_iterations() >= 1,
        "Should have measure iterations"
    );
}

/// Y6.1b: AprBenchmarkRunner can run decode benchmark
/// FALSIFICATION: benchmark_decode() fails or returns invalid results
#[test]
fn y6_1b_benchmark_decode_works() {
    use realizar::apr_transformer::{AprBenchmarkRunner, AprTransformer, AprTransformerConfig};

    let config = AprTransformerConfig {
        hidden_dim: 64,
        num_layers: 1,
        num_heads: 4,
        num_kv_heads: 4,
        vocab_size: 100,
        intermediate_dim: 128,
        context_length: 64,
        ..Default::default()
    };

    let transformer = AprTransformer::new(config);
    let mut runner = AprBenchmarkRunner::new(transformer);

    // Configure for fast test
    runner.set_warmup_iterations(1);
    runner.set_measure_iterations(5);

    // Prompt and generation config
    let prompt = vec![1u32, 2, 3];
    let num_tokens = 10;

    let result = runner.benchmark_decode(&prompt, num_tokens);

    assert!(result.is_ok(), "benchmark_decode should succeed");

    let bench = result.unwrap();
    assert!(bench.tokens_generated > 0, "Should generate some tokens");
    assert!(bench.total_time_ms > 0.0, "Should take some time");
    assert!(
        bench.tokens_per_second > 0.0,
        "Should have positive throughput"
    );
}

// ============================================================================
// Y6.2: Throughput Measurement
// ============================================================================

/// Y6.2a: Throughput measurement is accurate
/// FALSIFICATION: Measured throughput differs >10% from manual calculation
#[test]
fn y6_2a_throughput_accurate() {
    use realizar::apr_transformer::{AprBenchmarkRunner, AprTransformer, AprTransformerConfig};

    let config = AprTransformerConfig {
        hidden_dim: 64,
        num_layers: 1,
        num_heads: 4,
        num_kv_heads: 4,
        vocab_size: 100,
        intermediate_dim: 128,
        context_length: 64,
        ..Default::default()
    };

    let transformer = AprTransformer::new(config);
    let mut runner = AprBenchmarkRunner::new(transformer);
    runner.set_warmup_iterations(0);
    runner.set_measure_iterations(1);

    let prompt = vec![1u32, 2, 3];
    let num_tokens = 5;

    let result = runner.benchmark_decode(&prompt, num_tokens).unwrap();

    // Manual calculation
    let expected_throughput = (result.tokens_generated as f64) / (result.total_time_ms / 1000.0);
    let diff_pct =
        ((result.tokens_per_second - expected_throughput) / expected_throughput).abs() * 100.0;

    assert!(
        diff_pct < 10.0,
        "Throughput accuracy: measured={:.2}, expected={:.2}, diff={:.2}%",
        result.tokens_per_second,
        expected_throughput,
        diff_pct
    );
}

/// Y6.2b: Statistics include p50, p99, and std_dev
/// FALSIFICATION: Missing statistical metrics
#[test]
fn y6_2b_statistics_included() {
    use realizar::apr_transformer::{AprBenchmarkRunner, AprTransformer, AprTransformerConfig};

    let config = AprTransformerConfig {
        hidden_dim: 64,
        num_layers: 1,
        num_heads: 4,
        num_kv_heads: 4,
        vocab_size: 100,
        intermediate_dim: 128,
        context_length: 64,
        ..Default::default()
    };

    let transformer = AprTransformer::new(config);
    let mut runner = AprBenchmarkRunner::new(transformer);
    runner.set_warmup_iterations(1);
    runner.set_measure_iterations(10);

    let prompt = vec![1u32, 2, 3];
    let result = runner.benchmark_decode(&prompt, 5).unwrap();

    // Should have statistical metrics
    assert!(result.throughput_p50 > 0.0, "Should have p50");
    assert!(result.throughput_p99 > 0.0, "Should have p99");
    assert!(result.throughput_std_dev >= 0.0, "Should have std_dev");

    // p99 should be <= p50 (higher throughput is better, p99 is worst case)
    // Actually for throughput, p50 >= p99 (p99 is lower bound)
    assert!(
        result.throughput_p50 >= result.throughput_p99,
        "p50 ({:.2}) should be >= p99 ({:.2})",
        result.throughput_p50,
        result.throughput_p99
    );
}

// ============================================================================
// Y6.3: Decode Speed Threshold
// ============================================================================

/// Y6.3a: Benchmark can check against threshold
/// FALSIFICATION: meets_threshold() missing or incorrect
#[test]
fn y6_3a_threshold_check() {
    use realizar::apr_transformer::{AprBenchmarkRunner, AprTransformer, AprTransformerConfig};

    let config = AprTransformerConfig {
        hidden_dim: 64,
        num_layers: 1,
        num_heads: 4,
        num_kv_heads: 4,
        vocab_size: 100,
        intermediate_dim: 128,
        context_length: 64,
        ..Default::default()
    };

    let transformer = AprTransformer::new(config);
    let mut runner = AprBenchmarkRunner::new(transformer);
    runner.set_warmup_iterations(1);
    runner.set_measure_iterations(5);

    let prompt = vec![1u32, 2, 3];
    let result = runner.benchmark_decode(&prompt, 5).unwrap();

    // Check against various thresholds
    let very_low_threshold = 0.001; // Almost any model should pass
    let very_high_threshold = 1_000_000.0; // No model will pass

    assert!(
        result.meets_threshold(very_low_threshold),
        "Should meet very low threshold"
    );
    assert!(
        !result.meets_threshold(very_high_threshold),
        "Should not meet impossibly high threshold"
    );
}

/// Y6.3b: CPU threshold is 50 tok/s
/// FALSIFICATION: Wrong threshold constant
#[test]
fn y6_3b_cpu_threshold_constant() {
    use realizar::apr_transformer::APR_CPU_DECODE_THRESHOLD_TOK_S;

    assert_eq!(
        APR_CPU_DECODE_THRESHOLD_TOK_S, 50.0,
        "CPU decode threshold should be 50 tok/s per spec"
    );
}

// ============================================================================
// Y6.4: Prefill Benchmark (Y8 related)
// ============================================================================

/// Y6.4a: Prefill benchmark exists
/// FALSIFICATION: benchmark_prefill() missing
#[test]
fn y6_4a_prefill_benchmark_exists() {
    use realizar::apr_transformer::{AprBenchmarkRunner, AprTransformer, AprTransformerConfig};

    let config = AprTransformerConfig {
        hidden_dim: 64,
        num_layers: 1,
        num_heads: 4,
        num_kv_heads: 4,
        vocab_size: 100,
        intermediate_dim: 128,
        context_length: 128,
        ..Default::default()
    };

    let transformer = AprTransformer::new(config);
    let mut runner = AprBenchmarkRunner::new(transformer);
    runner.set_warmup_iterations(1);
    runner.set_measure_iterations(5);

    // Long prompt for prefill test
    let prompt: Vec<u32> = (0..50).collect();

    let result = runner.benchmark_prefill(&prompt);

    assert!(result.is_ok(), "benchmark_prefill should succeed");

    let bench = result.unwrap();
    assert!(bench.prompt_tokens > 0, "Should process prompt tokens");
    assert!(bench.prefill_time_ms > 0.0, "Should take some time");
    assert!(
        bench.prefill_tok_s > 0.0,
        "Should have positive prefill throughput"
    );
}

/// Y6.4b: Prefill threshold is 100 tok/s (Y8)
/// FALSIFICATION: Wrong threshold constant
#[test]
fn y6_4b_prefill_threshold_constant() {
    use realizar::apr_transformer::APR_PREFILL_THRESHOLD_TOK_S;

    assert_eq!(
        APR_PREFILL_THRESHOLD_TOK_S, 100.0,
        "Prefill threshold should be 100 tok/s per spec"
    );
}

// ============================================================================
// Y6.5: Format Parity Comparison
// ============================================================================

/// Y6.5a: Can compare APR benchmark to baseline
/// FALSIFICATION: compare_to_baseline() missing
#[test]
fn y6_5a_baseline_comparison() {
    use realizar::apr_transformer::{
        AprBenchmarkResult, AprBenchmarkRunner, AprTransformer, AprTransformerConfig,
    };

    let config = AprTransformerConfig {
        hidden_dim: 64,
        num_layers: 1,
        num_heads: 4,
        num_kv_heads: 4,
        vocab_size: 100,
        intermediate_dim: 128,
        context_length: 64,
        ..Default::default()
    };

    let transformer = AprTransformer::new(config);
    let mut runner = AprBenchmarkRunner::new(transformer);
    runner.set_warmup_iterations(1);
    runner.set_measure_iterations(5);

    let prompt = vec![1u32, 2, 3];
    let result = runner.benchmark_decode(&prompt, 5).unwrap();

    // Create a synthetic baseline result
    let baseline = AprBenchmarkResult {
        tokens_generated: 5,
        total_time_ms: 100.0,
        tokens_per_second: 50.0,
        throughput_p50: 50.0,
        throughput_p99: 45.0,
        throughput_std_dev: 2.0,
        ..Default::default()
    };

    let comparison = result.compare_to_baseline(&baseline);

    // Should have comparison metrics
    assert!(
        comparison.throughput_ratio.is_finite(),
        "Throughput ratio should be finite"
    );
    assert!(
        comparison.is_parity() || !comparison.is_parity(),
        "Parity check should return a boolean"
    );
}

/// Y6.5b: Parity requires >= 95% of baseline
/// FALSIFICATION: Wrong parity threshold
#[test]
fn y6_5b_parity_threshold() {
    use realizar::apr_transformer::APR_PARITY_THRESHOLD_PCT;

    assert!(
        (APR_PARITY_THRESHOLD_PCT - 95.0).abs() < 0.1,
        "Parity threshold should be 95% per spec"
    );
}

// ============================================================================
// Y6.6: Memory Efficiency (Y10 related)
// ============================================================================

/// Y6.6a: Memory measurement included in benchmark
/// FALSIFICATION: memory_mb missing from result
#[test]
fn y6_6a_memory_measurement() {
    use realizar::apr_transformer::{AprBenchmarkRunner, AprTransformer, AprTransformerConfig};

    let config = AprTransformerConfig {
        hidden_dim: 256,
        num_layers: 2,
        num_heads: 8,
        num_kv_heads: 8,
        vocab_size: 1000,
        intermediate_dim: 512,
        context_length: 128,
        ..Default::default()
    };

    let transformer = AprTransformer::new(config);
    let mut runner = AprBenchmarkRunner::new(transformer);
    runner.set_warmup_iterations(1);
    runner.set_measure_iterations(3);

    let prompt = vec![1u32, 2, 3];
    let result = runner.benchmark_decode(&prompt, 5).unwrap();

    // Should report memory usage
    assert!(result.peak_memory_mb > 0.0, "Should report memory usage");
    assert!(result.model_memory_mb > 0.0, "Should report model memory");
}

// ============================================================================
// Y6.7: Load Time (Y9 related)
// ============================================================================

/// Y6.7a: Load time benchmark exists
/// FALSIFICATION: benchmark_load() missing
#[test]
fn y6_7a_load_time_benchmark() {
    use realizar::apr_transformer::{AprBenchmarkRunner, AprTransformer, AprTransformerConfig};

    let config = AprTransformerConfig {
        hidden_dim: 256,
        num_layers: 2,
        num_heads: 8,
        num_kv_heads: 8,
        vocab_size: 1000,
        intermediate_dim: 512,
        context_length: 128,
        ..Default::default()
    };

    // Measure creation time (simulates load time)
    let load_result = AprBenchmarkRunner::benchmark_load(|| AprTransformer::new(config.clone()));

    assert!(load_result.is_ok(), "benchmark_load should succeed");

    let bench = load_result.unwrap();
    assert!(bench.load_time_ms > 0.0, "Should take some time to load");
}

// ============================================================================
// Summary: Y6 Popperian Falsification Matrix
// ============================================================================
//
// | Test | Claim | Falsification Condition |
// |------|-------|------------------------|
// | Y6.1a | AprBenchmarkRunner exists | Struct missing |
// | Y6.1b | benchmark_decode works | Method fails |
// | Y6.2a | Throughput accurate | >10% error |
// | Y6.2b | Statistics included | Missing p50/p99/std_dev |
// | Y6.3a | Threshold check works | meets_threshold() fails |
// | Y6.3b | CPU threshold = 50 | Wrong constant |
// | Y6.4a | Prefill benchmark exists | Method missing |
// | Y6.4b | Prefill threshold = 100 | Wrong constant |
// | Y6.5a | Baseline comparison works | compare_to_baseline() fails |
// | Y6.5b | Parity = 95% | Wrong constant |
// | Y6.6a | Memory measurement | memory_mb missing |
// | Y6.7a | Load time benchmark | benchmark_load() missing |
