realizar 0.8.5

/// GPU model backend.
#[cfg(feature = "gpu")]
fn try_gpu_completions(
    state: &AppState,
    request: &CompletionRequest,
    max_tokens: usize,
    temperature: f32,
    start: std::time::Instant,
) -> Result<Option<CompletionResponse>, RErr> {
    use crate::gpu::GpuGenerateConfig;

    let gpu_model_lock = match state.gpu_model() {
        Some(l) => l,
        None => return Ok(None),
    };
    let tokenizer = state.tokenizer.clone().ok_or_else(|| {
        rerr(
            state,
            StatusCode::INTERNAL_SERVER_ERROR,
            "No tokenizer available",
        )
    })?;
    let prompt_ids = tokenizer.encode(&request.prompt);
    if prompt_ids.is_empty() {
        return Err(rerr(
            state,
            StatusCode::BAD_REQUEST,
            "Prompt cannot be empty",
        ));
    }
    let prompt_tokens = prompt_ids.len();
    let prompt: Vec<usize> = prompt_ids.iter().map(|&id| id as usize).collect();

    let gpu_config = GpuGenerateConfig {
        max_tokens,
        temperature,
        top_k: 1,
        stop_tokens: Vec::new(),
        trace: state.is_trace_enabled(),
    };

    let mut gpu_model = gpu_model_lock.write().map_err(|e| {
        rerr(
            state,
            StatusCode::INTERNAL_SERVER_ERROR,
            format!("GPU lock: {e}"),
        )
    })?;
    let generated = gpu_model
        .generate(&prompt, &gpu_config)
        .map_err(|e| rerr(state, StatusCode::INTERNAL_SERVER_ERROR, e))?;

    let token_ids: Vec<u32> = generated
        .iter()
        .skip(prompt_tokens)
        .filter_map(|&id| u32::try_from(id).ok())
        .collect();
    let completion_tokens = token_ids.len();
    let text = tokenizer
        .decode(&token_ids)
        .map_err(|e| rerr(state, StatusCode::INTERNAL_SERVER_ERROR, e))?;
    state
        .metrics
        .record_success(completion_tokens, start.elapsed());

    let response_id = format!("cmpl-{}", &uuid::Uuid::new_v4().to_string()[..8]);
    Ok(Some(CompletionResponse {
        id: response_id,
        object: "text_completion".to_string(),
        created: epoch_secs(),
        model: request.model.clone(),
        choices: vec![CompletionChoice {
            text,
            index: 0,
            logprobs: None,
            finish_reason: "stop".to_string(),
        }],
        usage: Usage {
            prompt_tokens,
            completion_tokens,
            total_tokens: prompt_tokens + completion_tokens,
        },
    }))
}

/// CPU model fallback.
fn registry_completions(
    state: &AppState,
    request: &CompletionRequest,
    max_tokens: usize,
    temperature: f32,
    start: std::time::Instant,
) -> Result<CompletionResponse, RErr> {
    let model_id = if request.model == "default" || request.model.is_empty() {
        None
    } else {
        Some(request.model.as_str())
    };

    let (model, tokenizer) = state
        .get_model(model_id)
        .map_err(|e| rerr(state, StatusCode::NOT_FOUND, e))?;
    let prompt_ids = tokenizer.encode(&request.prompt);
    if prompt_ids.is_empty() {
        return Err(rerr(
            state,
            StatusCode::BAD_REQUEST,
            "Prompt cannot be empty",
        ));
    }
    let prompt_tokens = prompt_ids.len();
    let prompt: Vec<usize> = prompt_ids.iter().map(|&id| id as usize).collect();

    let mut config = GenerationConfig::default()
        .with_max_tokens(max_tokens)
        .with_temperature(temperature);
    if let Some(top_p) = request.top_p {
        config.strategy = SamplingStrategy::TopP { p: top_p as f32 };
    }

    let generated = model
        .generate(&prompt, &config)
        .map_err(|e| rerr(state, StatusCode::INTERNAL_SERVER_ERROR, e))?;
    let token_ids: Vec<u32> = generated
        .iter()
        .skip(prompt_tokens)
        .filter_map(|&id| u32::try_from(id).ok())
        .collect();
    let completion_tokens = token_ids.len();
    let text = tokenizer
        .decode(&token_ids)
        .map_err(|e| rerr(state, StatusCode::INTERNAL_SERVER_ERROR, e))?;
    state
        .metrics
        .record_success(completion_tokens, start.elapsed());

    Ok(completion_resp(
        "cmpl",
        request.model.clone(),
        text,
        prompt_tokens,
        completion_tokens,
        max_tokens,
    ))
}

/// ALB-098: Q4K GPU completions via dedicated inference thread.
#[cfg(feature = "cuda")]
async fn try_apr_q4k_completions(
    state: &AppState,
    request: &CompletionRequest,
    max_tokens: usize,
    temperature: f32,
    start: std::time::Instant,
) -> Result<Option<CompletionResponse>, RErr> {
    use crate::api::apr_q4k_scheduler::AprQ4kRequest;

    let q4k_tx = match state.apr_q4k_tx() {
        Some(tx) => tx,
        None => return Ok(None),
    };
    let tokenizer = state.tokenizer.clone().ok_or_else(|| {
        rerr(state, StatusCode::INTERNAL_SERVER_ERROR, "No tokenizer available")
    })?;
    let prompt_ids = tokenizer.encode(&request.prompt);
    if prompt_ids.is_empty() {
        return Err(rerr(state, StatusCode::BAD_REQUEST, "Prompt cannot be empty"));
    }
    let prompt_tokens = prompt_ids.len();

    let (response_tx, response_rx) = tokio::sync::oneshot::channel();
    // ALB-109: Get EOS token IDs from model config or tokenizer.
    let eos_ids = state.model_eos_ids();

    q4k_tx
        .send(AprQ4kRequest {
            prompt_ids,
            max_tokens,
            temperature,
            eos_ids,
            response_tx,
        })
        .await
        .map_err(|_| rerr(state, StatusCode::INTERNAL_SERVER_ERROR, "Q4K thread unavailable"))?;

    let result = response_rx
        .await
        .map_err(|_| rerr(state, StatusCode::INTERNAL_SERVER_ERROR, "Q4K thread dropped response"))?;

    let resp = result.map_err(|e| {
        rerr(state, StatusCode::INTERNAL_SERVER_ERROR, format!("Q4K generation failed: {e}"))
    })?;

    let text = tokenizer
        .decode(&resp.output_tokens)
        .map_err(|e| rerr(state, StatusCode::INTERNAL_SERVER_ERROR, e))?;
    let completion_tokens = resp.tokens_generated;
    state.metrics.record_success(completion_tokens, start.elapsed());

    Ok(Some(completion_resp(
        "cmpl",
        request.model.clone(),
        text,
        prompt_tokens,
        completion_tokens,
        max_tokens,
    )))
}

/// realizar#184 / ALB-136: CUDA GGUF completions via batch scheduler.
///
/// Root cause: `with_cuda_model_and_vocab()` sets `model: None` so the registry
/// fallback fails with "No model available". The CUDA GGUF model is only reachable
/// via `cuda_batch_tx` (the batch scheduler channel). This function bridges the gap
/// for non-streaming /v1/completions requests.
#[cfg(feature = "cuda")]
async fn try_cuda_gguf_completions(
    state: &AppState,
    request: &CompletionRequest,
    max_tokens: usize,
    temperature: f32,
    start: std::time::Instant,
) -> Result<Option<CompletionResponse>, RErr> {
    use crate::api::cuda_batch_scheduler::CudaBatchRequest;
    use crate::gguf::QuantizedGenerateConfig;

    let batch_tx = match state.cuda_batch_tx() {
        Some(tx) => tx,
        None => return Ok(None),
    };
    let tokenizer = state.tokenizer.clone().ok_or_else(|| {
        rerr(state, StatusCode::INTERNAL_SERVER_ERROR, "No tokenizer available")
    })?;
    let prompt_ids = tokenizer.encode(&request.prompt);
    if prompt_ids.is_empty() {
        return Err(rerr(state, StatusCode::BAD_REQUEST, "Prompt cannot be empty"));
    }
    let prompt_tokens = prompt_ids.len();

    let eos = state.cached_eos_token_id.unwrap_or(151643);
    let q_config = QuantizedGenerateConfig {
        max_tokens,
        temperature,
        stop_tokens: vec![eos],
        ..Default::default()
    };

    // Streaming channel — collect all tokens for non-streaming response
    let (token_tx, mut token_rx) = tokio::sync::mpsc::channel::<Result<u32, String>>(max_tokens + 1);

    let batch_req = CudaBatchRequest {
        prompt_ids,
        config: q_config,
        token_tx,
        enqueue_time: std::time::Instant::now(),
    };

    batch_tx
        .try_send(batch_req)
        .map_err(|_| rerr(state, StatusCode::SERVICE_UNAVAILABLE, "CUDA batch queue full"))?;

    // Collect all generated tokens
    let mut output_tokens = Vec::with_capacity(max_tokens);
    while let Some(result) = token_rx.recv().await {
        match result {
            Ok(token_id) => output_tokens.push(token_id),
            Err(e) => {
                return Err(rerr(state, StatusCode::INTERNAL_SERVER_ERROR, format!("CUDA generation: {e}")));
            }
        }
    }

    let completion_tokens = output_tokens.len();
    let mut text = tokenizer
        .decode(&output_tokens)
        .map_err(|e| rerr(state, StatusCode::INTERNAL_SERVER_ERROR, e))?;

    // Truncate at first stop sequence (OpenAI behavior)
    if let Some(stops) = &request.stop {
        for stop in stops {
            if let Some(pos) = text.find(stop.as_str()) {
                text.truncate(pos);
                break;
            }
        }
    }

    state.metrics.record_success(completion_tokens, start.elapsed());

    Ok(Some(completion_resp(
        "cmpl",
        request.model.clone(),
        text,
        prompt_tokens,
        completion_tokens,
        max_tokens,
    )))
}

pub async fn openai_completions_handler(
    State(state): State<AppState>,
    Json(request): Json<CompletionRequest>,
) -> Result<Json<CompletionResponse>, RErr> {
    let start = std::time::Instant::now();
    let max_tokens = request.max_tokens.unwrap_or(256);
    let temperature = request.temperature.unwrap_or(0.7) as f32;

    #[cfg(feature = "gpu")]
    if let Some(r) =
        try_cached_completions(&state, &request, max_tokens, temperature, start).await?
    {
        return Ok(Json(r));
    }

    if let Some(r) = try_quantized_completions(&state, &request, max_tokens, temperature, start)? {
        return Ok(Json(r));
    }

    #[cfg(feature = "gpu")]
    if let Some(r) = try_gpu_completions(&state, &request, max_tokens, temperature, start)? {
        return Ok(Json(r));
    }

    #[cfg(feature = "cuda")]
    if let Some(r) = try_apr_q4k_completions(&state, &request, max_tokens, temperature, start).await? {
        return Ok(Json(r));
    }

    // realizar#184 / ALB-136: CUDA GGUF models via batch scheduler
    #[cfg(feature = "cuda")]
    if let Some(r) = try_cuda_gguf_completions(&state, &request, max_tokens, temperature, start).await? {
        return Ok(Json(r));
    }

    Ok(Json(registry_completions(
        &state,
        &request,
        max_tokens,
        temperature,
        start,
    )?))
}

/// realizr#191: Logprobs endpoint for perplexity measurement (F-QUALITY-01).
///
/// Returns per-token log probabilities for the generated sequence.
/// Uses the direct CUDA path (not batch scheduler) to access logits.
///
/// POST /v1/logprobs { "prompt": "...", "max_tokens": 256 }
/// Returns { "tokens": [...], "logprobs": [...], "perplexity": ... }
#[cfg(feature = "cuda")]
pub async fn logprobs_handler(
    State(state): State<AppState>,
    Json(request): Json<CompletionRequest>,
) -> Result<Json<serde_json::Value>, RErr> {
    use crate::gguf::QuantizedGenerateConfig;

    let cuda_model_lock = state.cuda_model().ok_or_else(|| {
        rerr(&state, StatusCode::SERVICE_UNAVAILABLE, "No CUDA model loaded")
    })?;
    let tokenizer = state.tokenizer.clone().ok_or_else(|| {
        rerr(&state, StatusCode::INTERNAL_SERVER_ERROR, "No tokenizer")
    })?;

    let prompt_ids = tokenizer.encode(&request.prompt);
    if prompt_ids.is_empty() {
        return Err(rerr(&state, StatusCode::BAD_REQUEST, "Empty prompt"));
    }

    let max_tokens = request.max_tokens.unwrap_or(256);
    let eos = state.cached_eos_token_id.unwrap_or(151643);
    let config = QuantizedGenerateConfig {
        max_tokens,
        temperature: 0.0, // greedy for perplexity
        top_k: 1,
        stop_tokens: vec![eos],
        logprobs: true,
        ..Default::default()
    };

    let result = {
        let mut model = cuda_model_lock.write().expect("CUDA model lock");
        model.generate_gpu_resident_logprobs(
            &prompt_ids.iter().map(|&x| x as u32).collect::<Vec<_>>(),
            &config,
        ).map_err(|e| rerr(&state, StatusCode::INTERNAL_SERVER_ERROR, e))?
    };

    let prompt_len = prompt_ids.len();
    let gen_tokens: Vec<u32> = result.tokens[prompt_len..].to_vec();
    let gen_text: Vec<String> = gen_tokens.iter().map(|&t| {
        tokenizer.decode(&[t]).unwrap_or_else(|_| format!("<{t}>"))
    }).collect();

    // Compute perplexity: exp(-1/N * sum(logprobs))
    let n = result.logprobs.len() as f64;
    let sum_logprob: f64 = result.logprobs.iter().map(|lp| f64::from(lp.logprob)).sum();
    let perplexity = if n > 0.0 { (-sum_logprob / n).exp() } else { 0.0 };

    let logprobs_json: Vec<serde_json::Value> = result.logprobs.iter().zip(gen_text.iter()).map(|(lp, text)| {
        serde_json::json!({
            "token": text,
            "token_id": lp.token_id,
            "logprob": lp.logprob,
        })
    }).collect();

    Ok(Json(serde_json::json!({
        "prompt_tokens": prompt_len,
        "completion_tokens": gen_tokens.len(),
        "tokens": gen_text,
        "logprobs": logprobs_json,
        "perplexity": perplexity,
        "sum_logprob": sum_logprob,
    })))
}

/// realizr#191: Teacher-forcing perplexity endpoint (F-QUALITY-01).
///
/// Feeds ground-truth tokens through the model and measures how well
/// the model predicts each next token. Standard PPL methodology
/// matching llama-perplexity.
///
/// POST /v1/perplexity { "prompt": "<text>", "model": "default" }
/// Returns { "perplexity": 15.8, "num_tokens": 512 }
#[cfg(feature = "cuda")]
pub async fn perplexity_handler(
    State(state): State<AppState>,
    Json(request): Json<CompletionRequest>,
) -> Result<Json<serde_json::Value>, RErr> {
    let cuda_model_lock = state.cuda_model().ok_or_else(|| {
        rerr(&state, StatusCode::SERVICE_UNAVAILABLE, "No CUDA model loaded")
    })?;
    let tokenizer = state.tokenizer.clone().ok_or_else(|| {
        rerr(&state, StatusCode::INTERNAL_SERVER_ERROR, "No tokenizer")
    })?;

    let token_ids: Vec<u32> = tokenizer
        .encode(&request.prompt)
        .iter()
        .map(|&x| x as u32)
        .collect();
    if token_ids.len() < 2 {
        return Err(rerr(&state, StatusCode::BAD_REQUEST, "Need at least 2 tokens"));
    }

    let start = std::time::Instant::now();
    let ppl = {
        let mut model = cuda_model_lock.write().expect("CUDA model lock");
        model
            .perplexity_gpu_resident(&token_ids)
            .map_err(|e| rerr(&state, StatusCode::INTERNAL_SERVER_ERROR, e))?
    };
    let elapsed = start.elapsed();

    Ok(Json(serde_json::json!({
        "perplexity": ppl,
        "num_tokens": token_ids.len(),
        "elapsed_ms": elapsed.as_millis(),
        "tokens_per_sec": token_ids.len() as f64 / elapsed.as_secs_f64(),
    })))
}

/// OpenAI-compatible embeddings handler (/v1/embeddings)
pub async fn openai_embeddings_handler(
    State(state): State<AppState>,
    Json(request): Json<EmbeddingRequest>,
) -> Result<Json<EmbeddingResponse>, (StatusCode, Json<ErrorResponse>)> {
    // Delegate to native handler
    realize_embed_handler(State(state), Json(request)).await
}