use anyhow::Result;
use searchi_semantic::{Device, GemmaEmbedder};

#[test]
#[ignore = "Requires explicitly provisioned CPU Google tokenizer and worker"]
fn normalized_code_tokenization_preserves_original_source_windows() -> Result<()> {
    let models = std::env::var_os("SEARCHI_GEMMA_MODEL_DIR").expect("Set model directory");
    let worker = std::env::var_os("SEARCHI_GEMMA_WORKER").expect("Set CPU worker");
    let mut embedder = GemmaEmbedder::load_with_device(models, worker, 1, Device::Cpu)?;
    // Genuine frozen TypeScript fixture that does not roundtrip through the
    // tokenizer. Do not replace source bytes with normalized decoded tokens.
    let code = "export function process(rows: {category: string; amount: number}[]) {\n  const totals = new Map<string, number>();\n  for (const row of rows) totals.set(row.category, (totals.get(row.category) ?? 0) + row.amount);\n  return totals;\n}\n";
    for text in [
        code.to_owned(),
        format!("{code}// Grüße 日本語 🦀\n").repeat(160),
    ] {
        let windows = embedder.split_windows(&text, "c.ts")?;
        let mut covered = 0;
        for window in &windows {
            assert!(window.start_byte <= covered);
            assert!(window.end_byte > covered);
            assert_eq!(window.text, text[window.start_byte..window.end_byte]);
            covered = window.end_byte;
        }
        assert_eq!(covered, text.len());
        assert!(windows.len() <= 256);
        if text.len() > 1000 {
            assert!(windows.len() > 1);
        }
    }
    Ok(())
}

#[test]
#[ignore = "Requires explicitly provisioned CPU Google model and rebuilt code-capable worker"]
fn actual_code_task_differs_from_search_and_debug_preserves_vectors() -> Result<()> {
    let models = std::env::var_os("SEARCHI_GEMMA_MODEL_DIR").expect("Set model directory");
    let worker = std::env::var_os("SEARCHI_GEMMA_WORKER").expect("Set CPU worker");
    let mut embedder = GemmaEmbedder::load_with_device(models, worker, 1, Device::Cpu)?;
    assert!(embedder.info.code_query_supported);
    assert_eq!(embedder.info.device, "cpu");
    let query = "parse a JSON response and retry a failed network request";
    let ordinary = embedder.embed_query(query)?;
    let code = embedder.embed_code_query(query)?;
    let cosine = |a: &[f32], b: &[f32]| {
        a.iter()
            .zip(b)
            .map(|(a, b)| f64::from(*a) * f64::from(*b))
            .sum::<f64>()
    };
    assert!(
        cosine(&ordinary, &code) < 0.999999,
        "Task-specific prompt must not silently reuse generic search embeddings"
    );
    let debug = embedder.embed_code_query_debug(query)?;
    assert!(cosine(&code, &debug) > 0.999999);
    assert!(embedder
        .query_diagnostics()
        .contains_key("worker_token_count"));
    let ordinary_again = embedder.embed_query(query)?;
    assert!(
        cosine(&ordinary, &ordinary_again) > 0.999999,
        "Previous code query must not contaminate a retained encoder context"
    );
    Ok(())
}