dynamo-llm 1.6.2

// SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
// SPDX-License-Identifier: Apache-2.0

//! The Preprocessor consists of the following modules
//!
//! - `translation`: This module converts the allowed Ingress message types to the corresponding
//!   internal representation.
//! - `apply`: This module applies ModelConfig defaults to any empty optional fields specified
//! - `prompt`: This module applies any prompt template logic to the internal Request object.
//! - `tokenize`: This module tokenizes the formatted prompt string and returns the token ids.
//!
//! The Preprocessor will accept any IngressRequest and transform it to a BackendRequest.

pub mod media;
#[cfg(feature = "mm-routing")]
pub mod mm_routing;
pub mod prompt;
pub mod speculative_prefill;
pub(crate) mod structural_tag;
pub(crate) mod tool_choice;
pub mod tools;
use anyhow::Context;
use anyhow::{Result, bail};

use dynamo_protocols::types::{
    ChatCompletionMessageContent, ChatCompletionRequestMessage,
    ChatCompletionRequestToolMessageContent, ChatCompletionRequestToolMessageContentPart,
    ChatCompletionRequestUserMessageContent, ChatCompletionRequestUserMessageContentPart,
    ChatCompletionToolChoiceOption, EncodingFormat,
};
use dynamo_renderer::{OAIPromptFormatter, PromptRenderError, RenderedPrompt};
use dynamo_runtime::config::{
    env_is_falsey, environment_names::llm as env_llm, is_truthy, parse_bool_opt,
};
use dynamo_runtime::error::{DynamoError, ErrorType, PublicDetails};
use dynamo_runtime::telemetry::{LIFECYCLE_TRACE_CONTEXT_KEY, LifecycleStage, LifecycleTrace};
use either::Either;
use futures::Stream;
use futures::stream::{self, StreamExt};
use std::borrow::Cow;
use std::time::Instant;

use dynamo_runtime::dynamo_nvtx_range;
use dynamo_runtime::metrics::frontend_perf::{
    DETOKENIZE_TOKEN_COUNT, DETOKENIZE_TOTAL_US, STAGE_DURATION_SECONDS, STAGE_PREPROCESS,
    StageGuard, TEMPLATE_SECONDS, TOKENIZE_SECONDS,
};
use std::{
    any::Any,
    collections::{HashMap, HashSet},
    pin::Pin,
    sync::{Arc, Mutex, OnceLock},
};
use tokio_util::sync::CancellationToken;
use tracing::{self, Instrument};

#[cfg(all(feature = "mm-routing", feature = "media-ffmpeg"))]
use crate::local_model::runtime_config::{
    ModelRuntimeConfig, SGLANG_QWEN_VIDEO_PROCESSOR_CONTRACT_RUNTIME_KEY,
    VLLM_NEMOTRON_VIDEO_PROCESSOR_CONTRACT_RUNTIME_KEY,
    VLLM_QWEN_VIDEO_PROCESSOR_CONTRACT_RUNTIME_KEY,
};
use crate::local_model::runtime_config::{
    SGLANG_GENERATE_CAPABILITY, TOKEN_BUDGET_RUNTIME_KEY, TokenBudget,
};
#[cfg(feature = "mm-routing")]
use crate::model_card::ModelInfoType;
use crate::model_card::{ModelDeploymentCard, ModelInfo, PromptFormatterArtifact};
#[cfg(feature = "mm-routing")]
use crate::preprocessor::media::MediaFetcher;
use crate::preprocessor::media::{MediaDecoder, MediaLoader, max_data_url_bytes};
use crate::protocols::common::preprocessor::{
    MultimodalData, MultimodalDataMap, MultimodalUuidMap, PreprocessedRequestBuilder, RoutingHints,
};
use crate::protocols::common::timing::RequestTracker;
use crate::tokenizers::Encoding;

use dynamo_parsers::{
    ReasoningParser, ReasoningParserType, reasoning::ParserResult,
    tool_calling::parsers::get_tool_parser_map,
};
use dynamo_runtime::engine::{AsyncEngine, AsyncEngineContextProvider, ResponseStream};
use dynamo_runtime::pipeline::{
    AsyncEngineContext, Context as PipelineContext, Error, ManyOut, Operator, SingleIn, async_trait,
};
use dynamo_runtime::protocols::annotated::{Annotated, AnnotationsProvider};

use crate::protocols::{
    TokenIdType,
    common::{
        OutputOptionsProvider, SamplingOptionsProvider, StopConditionsProvider,
        extensions::{
            AgentHints, NvExtProvider, merge_response_nvext, request_cache_salt,
            routing_constraints_to_kv,
        },
    },
    openai::{
        DeltaGeneratorExt, ParsingOptions,
        chat_completions::{
            NvCreateChatCompletionRequest, NvCreateChatCompletionStreamResponse,
            scrub_synthetic_chunk_metadata,
        },
        completions::{NvCreateCompletionRequest, NvCreateCompletionResponse},
        embeddings::{NvCreateEmbeddingRequest, NvCreateEmbeddingResponse},
    },
};
use crate::tokenizers::traits::Tokenizer;

use crate::preprocessor::prompt::{
    MediaRequestExt, apply_continue_final_message, prompt_formatter_from_mdc,
};
use crate::protocols::openai::common_ext::CommonExtProvider;
use dynamo_renderer::{OAIChatLikeRequest, PromptFormatter, PromptInput, TextInput, TokenInput};

pub use crate::protocols::common::llm_backend::{BackendOutput, PreprocessedRequest};
pub use crate::protocols::common::metrics::{
    ANNOTATION_LLM_METRICS, ANNOTATION_PAYLOAD_USAGE, LLMMetricAnnotation,
};
pub use crate::protocols::common::preprocessor::PreprocessedEmbeddingRequest;

use crate::protocols::common::llm_backend::EmbeddingsEngineOutput;

fn routing_priorities(hints: Option<&AgentHints>) -> (Option<f64>, Option<u32>, Option<i32>) {
    let priority_jump = hints.and_then(|h| {
        h.priority
            .map(|priority| priority.max(0) as f64)
            .or(h.latency_sensitivity)
    });
    let strict_priority = hints.and_then(|h| h.strict_priority);
    let priority = hints.and_then(|h| h.priority);
    (priority_jump, strict_priority, priority)
}

/// Build a private validation diagnostic. Callers may attach structured `PublicDetails` only when every value is safe for clients.
pub(crate) fn invalid_argument_error(message: impl Into<String>) -> anyhow::Error {
    DynamoError::builder()
        .error_type(ErrorType::InvalidArgument)
        .message(message.into())
        .build()
        .into()
}

// Preserves terminal versus recoverable failures when moka shares a
// dimension fetch among concurrent callers.
#[cfg(feature = "mm-routing")]
enum ImageDimFetchFailure {
    InvalidArgument(String),
    Recoverable(String),
}

#[cfg(feature = "mm-routing")]
impl ImageDimFetchFailure {
    fn from_error(error: anyhow::Error) -> Self {
        if MediaFetcher::is_policy_rejection(&error) {
            Self::InvalidArgument(error.to_string())
        } else {
            Self::Recoverable(error.to_string())
        }
    }

    fn to_error(&self) -> anyhow::Error {
        match self {
            Self::InvalidArgument(message) => invalid_argument_error(message.clone()),
            Self::Recoverable(message) => anyhow::anyhow!("fetch_image_dims failed: {message}"),
        }
    }
}

fn validate_legacy_jail_nvext_choice_count(
    n: u8,
    extra_fields: Option<&[String]>,
    is_legacy_jail: bool,
) -> Result<()> {
    if n <= 1 || !is_legacy_jail {
        return Ok(());
    }

    const CHOICE_SPECIFIC_FIELDS: [&str; 3] = ["engine_data", "routed_experts", "stop_reason"];
    if let Some(field) = extra_fields.and_then(|fields| {
        fields
            .iter()
            .find(|field| CHOICE_SPECIFIC_FIELDS.contains(&field.as_str()))
    }) {
        return Err(invalid_argument_error(format!(
            "legacy tool-call parsing requires n = 1 when nvext.extra_fields requests choice-specific field `{field}`"
        )));
    }

    Ok(())
}

/// Prompt-opened reasoning for the parser route selected after guided decoding.
///
/// Native Muse channels and the legacy Basic reasoning parser use different
/// delimiters. Keep both facts until the request's actual route is known.
#[derive(Clone, Copy, Debug)]
pub struct PromptReasoningPrefill {
    legacy: bool,
    unified: bool,
    prefix: Option<&'static str>,
}

impl From<bool> for PromptReasoningPrefill {
    /// An explicit boolean describes the caller's already-selected parser.
    fn from(value: bool) -> Self {
        Self {
            legacy: value,
            unified: value,
            prefix: None,
        }
    }
}

impl PromptReasoningPrefill {
    fn for_route(self, route: &ToolProcessingRoute) -> bool {
        match route {
            ToolProcessingRoute::MuseUnified(_) | ToolProcessingRoute::Unified(_) => self.unified,
            _ => self.legacy,
        }
    }
}

#[derive(Clone, Debug, PartialEq, Eq)]
enum ToolProcessingRoute {
    MuseUnified(&'static str),
    Unified(&'static str),
    ParserV2(String),
    LegacyJail(Option<String>),
    PassThrough,
}

impl ToolProcessingRoute {
    fn uses_legacy_jail(&self) -> bool {
        matches!(self, Self::LegacyJail(_))
    }
}

fn tool_content_part_as_user(
    part: &ChatCompletionRequestToolMessageContentPart,
) -> Cow<'_, ChatCompletionRequestUserMessageContentPart> {
    Cow::Owned(match part {
        ChatCompletionRequestToolMessageContentPart::Text(part) => {
            ChatCompletionRequestUserMessageContentPart::Text(part.clone())
        }
        ChatCompletionRequestToolMessageContentPart::ImageUrl(part) => {
            ChatCompletionRequestUserMessageContentPart::ImageUrl(part.clone())
        }
        ChatCompletionRequestToolMessageContentPart::VideoUrl(part) => {
            ChatCompletionRequestUserMessageContentPart::VideoUrl(part.clone())
        }
        ChatCompletionRequestToolMessageContentPart::AudioUrl(part) => {
            ChatCompletionRequestUserMessageContentPart::AudioUrl(part.clone())
        }
    })
}

enum MultimodalContentPart<'a> {
    User(&'a ChatCompletionRequestUserMessageContentPart),
    Tool(&'a ChatCompletionRequestToolMessageContentPart),
}

impl<'a> MultimodalContentPart<'a> {
    fn as_user(&self) -> Cow<'a, ChatCompletionRequestUserMessageContentPart> {
        match self {
            Self::User(part) => Cow::Borrowed(part),
            Self::Tool(part) => tool_content_part_as_user(part),
        }
    }

    fn media_info(&self) -> Option<(&'static str, Option<url::Url>, Option<String>)> {
        match self {
            Self::User(part) => match *part {
                ChatCompletionRequestUserMessageContentPart::ImageUrl(part) => Some((
                    "image_url",
                    part.image_url.as_ref().map(|media| media.url.clone()),
                    part.uuid.clone(),
                )),
                ChatCompletionRequestUserMessageContentPart::VideoUrl(part) => Some((
                    "video_url",
                    part.video_url.as_ref().map(|media| media.url.clone()),
                    part.uuid.clone(),
                )),
                ChatCompletionRequestUserMessageContentPart::AudioUrl(part) => Some((
                    "audio_url",
                    part.audio_url.as_ref().map(|media| media.url.clone()),
                    part.uuid.clone(),
                )),
                _ => None,
            },
            Self::Tool(part) => match *part {
                ChatCompletionRequestToolMessageContentPart::ImageUrl(part) => Some((
                    "image_url",
                    part.image_url.as_ref().map(|media| media.url.clone()),
                    part.uuid.clone(),
                )),
                ChatCompletionRequestToolMessageContentPart::VideoUrl(part) => Some((
                    "video_url",
                    part.video_url.as_ref().map(|media| media.url.clone()),
                    part.uuid.clone(),
                )),
                ChatCompletionRequestToolMessageContentPart::AudioUrl(part) => Some((
                    "audio_url",
                    part.audio_url.as_ref().map(|media| media.url.clone()),
                    part.uuid.clone(),
                )),
                _ => None,
            },
        }
    }
}

fn multimodal_content_parts(
    message: &ChatCompletionRequestMessage,
) -> Option<impl Iterator<Item = MultimodalContentPart<'_>>> {
    match message {
        ChatCompletionRequestMessage::User(user) => match &user.content {
            ChatCompletionRequestUserMessageContent::Array(parts) => {
                Some(Either::Left(parts.iter().map(MultimodalContentPart::User)))
            }
            ChatCompletionRequestUserMessageContent::Text(_) => None,
        },
        ChatCompletionRequestMessage::Tool(tool) => match &tool.content {
            ChatCompletionRequestToolMessageContent::Array(parts) => {
                Some(Either::Right(parts.iter().map(MultimodalContentPart::Tool)))
            }
            ChatCompletionRequestToolMessageContent::Text(_) => None,
        },
        _ => None,
    }
}

#[cfg(feature = "mm-routing")]
fn image_content_part_url(
    content_part: &ChatCompletionRequestUserMessageContentPart,
) -> Option<&str> {
    let ChatCompletionRequestUserMessageContentPart::ImageUrl(part) = content_part else {
        return None;
    };
    part.image_url.as_ref().map(|image| image.url.as_str())
}

/// Decode a base64-encoded little-endian f32 byte string back into a float
/// vector. The byte length must be a multiple of 4; trailing bytes are
/// rejected. Shared by the tokens-path postprocessor and the HTTP embedding
/// handler, which converts leftover Base64 payloads to Float when requested.
pub(crate) fn decode_base64_to_floats(encoded: &str) -> Result<Vec<f32>, String> {
    use base64::{Engine as _, engine::general_purpose::STANDARD};
    let bytes = STANDARD
        .decode(encoded)
        .map_err(|error| error.to_string())?;
    if bytes.len() % std::mem::size_of::<f32>() != 0 {
        return Err(format!(
            "base64-decoded embedding byte length {} is not a multiple of 4",
            bytes.len()
        ));
    }
    Ok(bytes
        .chunks_exact(std::mem::size_of::<f32>())
        .map(|chunk| f32::from_le_bytes([chunk[0], chunk[1], chunk[2], chunk[3]]))
        .collect())
}

#[cfg(test)]
mod embedding_base64_transport_tests {
    use super::{OpenAIPreprocessor, decode_base64_to_floats};
    use crate::protocols::common::llm_backend::EmbeddingsEngineOutput;
    use crate::protocols::openai::embeddings::NvCreateEmbeddingRequest;
    use base64::{Engine as _, engine::general_purpose::STANDARD};
    use dynamo_runtime::protocols::annotated::Annotated;
    use futures::StreamExt;
    use futures::stream;

    #[test]
    fn portable_embedding_bytes_decode() {
        let expected = vec![0.0, 1.0, -1.0, 2.5, -42.5, 3.25, f32::MIN, f32::MAX];
        let bytes = expected
            .iter()
            .flat_map(|value: &f32| value.to_le_bytes())
            .collect::<Vec<_>>();
        let encoded = STANDARD.encode(bytes);
        assert_eq!(decode_base64_to_floats(&encoded).unwrap(), expected);
    }

    #[test]
    fn portable_embedding_bytes_reject_invalid_base64() {
        assert!(decode_base64_to_floats("not!valid!base64").is_err());
    }

    #[test]
    fn portable_embedding_bytes_reject_partial_float() {
        let encoded = STANDARD.encode([0_u8; 5]);
        let error = decode_base64_to_floats(&encoded).unwrap_err();
        assert!(error.contains("not a multiple of 4"));
    }

    #[test]
    fn empty_embeddings_engine_output_surfaces_as_error() {
        let request: NvCreateEmbeddingRequest = serde_json::from_value(serde_json::json!({
            "model": "test-model",
            "input": "hello"
        }))
        .unwrap();
        let output = Annotated::from_data(EmbeddingsEngineOutput {
            embeddings: vec![],
            prompt_tokens: 0,
            total_tokens: 0,
        });
        let items = futures::executor::block_on(
            OpenAIPreprocessor::transform_embedding_postprocessor_stream(
                stream::iter(vec![output]),
                request,
            )
            .collect::<Vec<_>>(),
        );
        assert_eq!(items.len(), 1);
        let item = items.into_iter().next().unwrap();
        assert!(item.is_error(), "empty embeddings must be a stream error");
        let err = item.into_result().unwrap_err();
        assert!(
            err.to_string().contains("empty `embeddings` field"),
            "error should name the empty embeddings field, got: {err}"
        );
    }
}

pub const ANNOTATION_FORMATTED_PROMPT: &str = "formatted_prompt";
pub const ANNOTATION_TOKEN_IDS: &str = "token_ids";
pub(crate) const DEFAULT_THINKING_MODE_RUNTIME_KEY: &str = "default_thinking_mode";

/// Drain a standalone router's forwarded `routing_data` onto this request's tracker so the
/// frontend's timing/worker/token surfaces populate, then drop the field to keep it off the
/// client wire. Worker attribution is drained so `build_response_nvext` can surface it on the
/// split-router query-only (`query_instance_id`) path; it is first-write-wins, so the
/// frontend's own recordings (when present) take precedence over the forwarded values.
fn drain_router_routing_data(
    data: &mut Option<BackendOutput>,
    tracker: Option<&crate::protocols::common::timing::RequestTracker>,
) {
    let Some(routing_data) = data.as_mut().and_then(|d| d.routing_data.take()) else {
        return;
    };
    let Some(tracker) = tracker else {
        return;
    };
    if let Some(timing) = routing_data.timing {
        tracker.set_external_timing(timing);
    }
    if let Some(worker_id) = routing_data.worker_id {
        tracker.set_external_worker_info(worker_id);
    }
    if let Some(token_ids) = routing_data.token_ids {
        tracker.set_external_query_token_ids(token_ids);
    }
}

fn attach_metrics_annotation<Resp>(response: &mut Annotated<Resp>, metrics: &LLMMetricAnnotation) {
    if let Ok(metrics_annotated) = metrics.to_annotation::<()>() {
        response.event = metrics_annotated.event;
        response.comment = metrics_annotated.comment;
    } else {
        tracing::warn!("Failed to serialize LLM metrics annotation");
    }
}

fn attach_llm_metrics<Resp>(response: &mut Annotated<Resp>, metrics: LLMMetricAnnotation)
where
    Resp: 'static,
{
    if response.event.is_some() {
        return;
    }

    if let Some(data) = response.data.as_mut()
        && let Some(chat_response) =
            (data as &mut dyn Any).downcast_mut::<NvCreateChatCompletionStreamResponse>()
    {
        chat_response.llm_metrics = Some(metrics);
        return;
    }

    attach_metrics_annotation(response, &metrics);
}

#[inline]
fn build_llm_metric_annotation(
    tracker: Option<&RequestTracker>,
    input_tokens: usize,
    output_tokens: usize,
    chunk_tokens: usize,
    cached_tokens: Option<usize>,
    mm_counts: MultimodalCounts,
    image_tokens: Option<usize>,
) -> LLMMetricAnnotation {
    LLMMetricAnnotation {
        input_tokens,
        output_tokens,
        chunk_tokens,
        cached_tokens,
        image_count: mm_counts.image,
        video_count: mm_counts.video,
        audio_count: mm_counts.audio,
        image_tokens,
        prefill_worker_id: tracker.and_then(|t| t.prefill_worker_id()),
        prefill_dp_rank: tracker.and_then(|t| t.prefill_dp_rank()),
        prefill_worker_type: tracker
            .and_then(|t| t.prefill_worker_type())
            .map(String::from),
        decode_worker_id: tracker.and_then(|t| t.decode_worker_id()),
        decode_dp_rank: tracker.and_then(|t| t.decode_dp_rank()),
        decode_worker_type: tracker
            .and_then(|t| t.decode_worker_type())
            .map(String::from),
        tokenize_latency: tracker.and_then(|t| t.tokenize_latency()),
        detokenize_total_latency: tracker.and_then(|t| t.detokenize_total_latency()),
        detokenize_count: tracker.map(|t| t.detokenize_count()),
    }
}

// Reasoning State for reasoning parsing transformation step.
//
// The reasoning parser and the guided-JSON bypass decision are kept per
// `choice.index` so that with `n > 1` one choice's bare-JSON bypass cannot
// suppress another choice's reasoning split. This mirrors the per-choice state
// already used by the tool-call jail and the leading-`<think>` strip stage.
struct ChoiceReasoningState {
    parser: Box<dyn ReasoningParser>,
    guided_json_bypass_decision: Option<bool>,
    // Reasoning text held back while it is still ambiguous. Only used on the
    // `defer_reasoning_for_nonempty_content` path — see the buffering note on
    // `ReasoningState`.
    pending_reasoning: String,
    // Whitespace-only normal text seen while still ambiguous. Held rather than
    // emitted so the streaming path agrees with the aggregator, which counts
    // whitespace-only content as empty.
    pending_content: String,
    // Set once this choice's parser has produced normal text, which for these
    // force-reasoning parsers means it left the reasoning block. From then on
    // the ambiguity is gone and deltas pass straight through.
    left_reasoning: bool,
    // Set once this choice has been drained, so the terminal-chunk drain and the
    // end-of-stream fallback cannot both emit the same bytes. Reopened if the
    // backend keeps sending content after `finish_reason`, so those bytes still
    // get a destination.
    drained: bool,
    // Set the first time this choice's parser is finalized and NEVER reset, even
    // when `drained` is reopened. `ReasoningParser::finish_reasoning_stream` is
    // not documented to be idempotent, so calling it twice could re-emit text a
    // parser had already handed over. Draining again is fine; finalizing again
    // is not.
    parser_finished: bool,
}

/// Estimates reasoning-token usage from the parser-classified Chat Completion stream.
///
/// This is intentionally chunk-granular: if one decoded chunk contains both reasoning
/// and visible content, every token in that chunk is counted as reasoning. The estimate
/// therefore overcounts visible-content tokens in mixed chunks. A positive count supplied
/// by the backend remains authoritative.
#[derive(Debug, Default)]
struct ReasoningUsageEstimator {
    total: u32,
    active_choices: HashSet<u32>,
}

impl ReasoningUsageEstimator {
    fn observe(&mut self, chunk: &NvCreateChatCompletionStreamResponse) {
        let token_count = chunk
            .llm_metrics
            .as_ref()
            .map_or(0, |metrics| metrics.chunk_tokens)
            .try_into()
            .unwrap_or(u32::MAX);

        let has_reasoning = chunk
            .inner
            .choices
            .iter()
            .any(|choice| choice.delta.reasoning_content.is_some());
        let active_without_visible_output = chunk.inner.choices.iter().any(|choice| {
            self.active_choices.contains(&choice.index) && !choice_has_visible_output(choice)
        });
        let usage_chunk_while_reasoning =
            chunk.inner.choices.is_empty() && !self.active_choices.is_empty();

        if token_count > 0
            && (has_reasoning || active_without_visible_output || usage_chunk_while_reasoning)
        {
            self.total = self.total.saturating_add(token_count);
        }

        for choice in &chunk.inner.choices {
            if choice.delta.reasoning_content.is_some() {
                self.active_choices.insert(choice.index);
            } else if choice_has_visible_output(choice) {
                self.active_choices.remove(&choice.index);
            }
        }
    }

    fn annotate(&self, usage: &mut dynamo_protocols::types::CompletionUsage) {
        let details = usage.completion_tokens_details.get_or_insert_default();
        if details.reasoning_tokens.unwrap_or(0) == 0 {
            details.reasoning_tokens = Some(self.total);
        }
    }
}

fn choice_has_visible_output(choice: &dynamo_protocols::types::ChatChoiceStream) -> bool {
    choice.delta.content.is_some()
        || choice
            .delta
            .tool_calls
            .as_ref()
            .is_some_and(|calls| !calls.is_empty())
}

fn annotate_reasoning_usage<S>(
    stream: S,
) -> impl Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send
where
    S: Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send + 'static,
{
    let mut estimator = ReasoningUsageEstimator::default();
    stream.map(move |mut response| {
        if let Some(chunk) = response.data.as_mut() {
            estimator.observe(chunk);
            if let Some(usage) = chunk.inner.usage.as_mut() {
                estimator.annotate(usage);
            }
        }
        response
    })
}

/// Drain what a choice still holds on the `defer_reasoning_for_nonempty_content`
/// path, returning `(content, reasoning_content)` to add to its delta. Shared by
/// the terminal-chunk drain and the end-of-stream fallback so the two cannot
/// disagree about which channel the leftover bytes belong to.
fn drain_deferred_reasoning(state: &mut ChoiceReasoningState) -> (Option<String>, Option<String>) {
    if state.drained {
        return (None, None);
    }
    state.drained = true;
    let pending = std::mem::take(&mut state.pending_reasoning);
    let pending_content = std::mem::take(&mut state.pending_content);
    // Finalize at most once per choice; see `parser_finished`.
    let result = if state.parser_finished {
        ParserResult::default()
    } else {
        state.parser_finished = true;
        state.parser.finish_reasoning_stream()
    };

    if state.left_reasoning {
        // An answer already streamed as content, so the non-empty-content
        // promise is met and the parser's own split stands: a later truncated
        // reasoning block stays reasoning instead of being appended to the
        // answer. `pending_content` is empty here — it was released with the
        // answer when the choice left reasoning.
        let mut content = pending_content;
        content.push_str(&result.normal_text);
        (
            (!content.is_empty()).then_some(content),
            (!result.reasoning_text.is_empty()).then_some(result.reasoning_text),
        )
    } else {
        // No real answer ever arrived, so everything held is all the response
        // has and must land in content — including a truncated `<think>` prefix,
        // which the parser reports as reasoning or normal text depending on
        // where it stopped. Held whitespace is dropped rather than prepended when
        // there is reasoning to surface, matching the aggregator, which replaces
        // whitespace-only content with the reasoning text instead of
        // concatenating.
        let mut text = pending;
        text.push_str(&result.reasoning_text);
        text.push_str(&result.normal_text);
        if text.is_empty() {
            // ...but if whitespace is genuinely all the turn produced, it is the
            // whole response. Returning nothing here would hand back an empty
            // `content` to a request that explicitly asked for non-empty content.
            text = pending_content;
        }
        ((!text.is_empty()).then_some(text), None)
    }
}

struct ReasoningState {
    stream: Pin<Box<dyn Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send>>,
    parser_name: String,
    prompt_injected_reasoning: bool,
    bypass_bare_guided_json: bool,
    choices: HashMap<u32, ChoiceReasoningState>,
    // Last emitted content-bearing response, reused as the envelope to carry any
    // text the parsers are still buffering when the upstream stream ends. Only
    // retained when `defer_reasoning_for_nonempty_content` is set, to keep the
    // per-token clone off the common reasoning hot path. Chunks with no choices
    // (the trailing usage-only chunk) are never retained — that envelope has no
    // delta slot to attach the flushed bytes to, so the flush would be dropped.
    last_response: Option<Annotated<NvCreateChatCompletionStreamResponse>>,
    // Nemotron force-reasoning parsers start inside the reasoning block, so
    // leading model output with no `<think>` is reported as reasoning even when
    // it is really the whole answer. Under `force_nonempty_content=true` the
    // chat template promises non-empty `content`, so emitting that text as
    // `reasoning_content` would leave `content` empty — the exact failure the
    // flag exists to prevent.
    //
    // When this is set, reasoning deltas are held in each choice's
    // `pending_reasoning` until the ambiguity resolves:
    //   - the parser produces normal text (it left the reasoning block), so the
    //     buffer really was reasoning: emit it as `reasoning_content` and stream
    //     the answer as `content` from then on; or
    //   - the stream ends first, so no answer is coming: emit the buffer (plus
    //     anything `finish_reasoning_stream` still holds, e.g. a truncated
    //     `<think>` prefix) as `content`.
    //
    // The cost is real and worth stating plainly: everything held is delivered
    // in one delta rather than token by token. For a turn that does reason, that
    // is the `reasoning_content` up to `</think>`, after which the answer streams
    // normally. For a turn with no `</think>` at all the held text IS the answer,
    // so the whole answer lands in a single delta on the terminal chunk — on
    // `main` (parser off, leading `<think>` stripped) that case streamed
    // incrementally.
    //
    // There is no cheaper split available: these parsers emit reasoning with no
    // opening `<think>` (see the `nemotron_nano` mapping in dynamo-parsers), so
    // until `</think>` arrives the same bytes are equally consistent with
    // reasoning and with a plain answer. Emitting eagerly is what leaked reasoning
    // into `content` in the first place. Scoped to the flag so no other parser
    // pays it.
    defer_reasoning_for_nonempty_content: bool,
    // Set once the backend has sent an error annotation. The end-of-stream
    // flush must not run after that: an error is terminal, and emitting
    // recovered reasoning as ordinary `content` after it would put text on the
    // wire that the generation never successfully produced.
    //
    // This matters because the flush is the only place bytes can arrive after
    // the last upstream chunk. `/v1/chat/completions` ends its SSE on the error
    // and never sees it, but `/v1/responses` and `/v1/messages` both record the
    // error and keep consuming (`saw_error = true; continue;`), so without this
    // latch they emit the synthetic content chunk and only then report failure.
    saw_terminal_error: bool,
}

#[derive(Default)]
struct DeferredUnifiedChoice {
    pending_reasoning: String,
    pending_content: String,
    saw_visible_output: bool,
    drained: bool,
}

impl DeferredUnifiedChoice {
    fn release_split(&mut self) -> (Option<String>, Option<String>) {
        let content = std::mem::take(&mut self.pending_content);
        let reasoning = std::mem::take(&mut self.pending_reasoning);
        (
            (!content.is_empty()).then_some(content),
            (!reasoning.is_empty()).then_some(reasoning),
        )
    }

    fn drain(&mut self) -> (Option<String>, Option<String>) {
        if self.drained {
            return (None, None);
        }
        self.drained = true;
        if self.saw_visible_output {
            return self.release_split();
        }
        let reasoning = std::mem::take(&mut self.pending_reasoning);
        let content = std::mem::take(&mut self.pending_content);
        if !reasoning.trim().is_empty() {
            (Some(reasoning), None)
        } else {
            ((!content.is_empty()).then_some(content), None)
        }
    }
}

/// Per-image routing payload accumulated by `gather_multi_modal_data` and
/// consumed by `gather_mm_exact_routing_info`.
#[derive(Debug, Clone, Copy)]
pub struct MmImageEntry {
    pub mm_hash: u64,
    pub width: u32,
    pub height: u32,
}

/// One replacement tracked in both the worker-visible and canonical routing
/// token spaces. Blocks with an exact placeholder/object mapping use the
/// canonical pad-value form. For a timestamp/delimiter-only boundary block,
/// the worker contract determines whether the hash also includes MM metadata.
#[cfg(feature = "mm-routing")]
#[derive(Debug, Clone, PartialEq, Eq)]
struct TrackedMmRoutingReplacement {
    mm_hash: u64,
    target_tokens: Vec<TokenIdType>,
    worker_tokens: Vec<TokenIdType>,
    routing_tokens: Vec<TokenIdType>,
}

/// Modality-aware routing payload accumulated in original message order.
#[cfg_attr(
    not(feature = "mm-routing"),
    allow(
        dead_code,
        reason = "the default build keeps the shared media-gathering return type but emits no routing entries"
    )
)]
#[derive(Debug, Clone)]
enum MmRoutingEntry {
    Image {
        mm_hash: u64,
        width: u32,
        height: u32,
    },
    #[cfg_attr(
        not(feature = "media-ffmpeg"),
        allow(dead_code, reason = "video entries require the FFmpeg media decoder")
    )]
    Video {
        mm_hash: u64,
        placeholder_token_id: TokenIdType,
        event_video_token_id: Option<TokenIdType>,
        target_tokens: Vec<TokenIdType>,
        replacement_tokens: Vec<TokenIdType>,
    },
}

#[cfg(feature = "mm-routing")]
impl MmRoutingEntry {
    fn mm_hash(&self) -> u64 {
        match self {
            Self::Image { mm_hash, .. } | Self::Video { mm_hash, .. } => *mm_hash,
        }
    }
}

#[cfg(feature = "mm-routing")]
fn exact_mm_routing_layout_accepts_next_entry(
    previous_entry_was_video: &mut bool,
    current_entry_is_video: bool,
) -> bool {
    // Adjacent video placeholders cannot be mapped unambiguously to vLLM KV
    // events. Keep this transition shared by the pre-decode and final checks.
    let accepted = !(*previous_entry_was_video && current_entry_is_video);
    *previous_entry_was_video = current_entry_is_video;
    accepted
}

#[cfg(feature = "mm-routing")]
fn exact_mm_routing_entries_are_unambiguous(entries: &[MmRoutingEntry]) -> bool {
    let mut previous_entry_was_video = false;
    entries.iter().all(|entry| {
        exact_mm_routing_layout_accepts_next_entry(
            &mut previous_entry_was_video,
            matches!(entry, MmRoutingEntry::Video { .. }),
        )
    })
}

#[cfg(feature = "mm-routing")]
fn routing_bos_to_prepend(
    configured_bos: Option<TokenIdType>,
    has_image_entry: bool,
) -> Option<TokenIdType> {
    has_image_entry.then_some(configured_bos).flatten()
}
#[cfg(feature = "mm-routing")]
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
enum RoutingImagePromptLayout {
    RepeatedPad,
    WrappedRepeatedPad {
        image_start: TokenIdType,
        image_end: TokenIdType,
    },
    KimiK3 {
        media_begin: TokenIdType,
        media_content: TokenIdType,
        media_end: TokenIdType,
    },
}

#[cfg(feature = "mm-routing")]
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
enum RoutingImageDimensionPolicy {
    Encoded,
    ExifTransposed,
}

#[cfg(feature = "mm-routing")]
fn runtime_has_vllm_generate_capability(
    runtime_config: &crate::local_model::runtime_config::ModelRuntimeConfig,
) -> bool {
    use crate::local_model::runtime_config::VLLM_INFERENCE_V1_GENERATE_CAPABILITY;

    runtime_config
        .get_engine_specific::<bool>(VLLM_INFERENCE_V1_GENERATE_CAPABILITY)
        .ok()
        .flatten()
        .unwrap_or(false)
}

#[cfg(feature = "mm-routing")]
fn routing_image_dimension_policy(
    runtime_config: &crate::local_model::runtime_config::ModelRuntimeConfig,
    frontend_decoding: bool,
    prompt_layout: Option<RoutingImagePromptLayout>,
) -> RoutingImageDimensionPolicy {
    if runtime_has_vllm_generate_capability(runtime_config)
        && !frontend_decoding
        && matches!(prompt_layout, Some(RoutingImagePromptLayout::KimiK3 { .. }))
    {
        RoutingImageDimensionPolicy::ExifTransposed
    } else {
        RoutingImageDimensionPolicy::Encoded
    }
}

#[cfg(feature = "mm-routing")]
fn encode_routing_segment(
    tokenizer: &dyn Tokenizer,
    text: &str,
    allow_special: bool,
) -> Result<Vec<TokenIdType>> {
    let segment = crate::tokenizers::EncodeSegment::new(text, allow_special);
    Ok(tokenizer.encode_segments(&[segment])?.token_ids().to_vec())
}

#[cfg(feature = "mm-routing")]
fn resolve_routing_image_prompt_layout(
    tokenizer: &dyn Tokenizer,
    kind: mm_routing::image::ImagePromptKind,
    expected_image_token_id: Option<TokenIdType>,
) -> Result<RoutingImagePromptLayout> {
    if kind == mm_routing::image::ImagePromptKind::RepeatedPad {
        return Ok(RoutingImagePromptLayout::RepeatedPad);
    }

    let resolve_segmented_control = |label: &str, token: &str| -> Result<TokenIdType> {
        let ids = encode_routing_segment(tokenizer, token, true)?;
        if ids.len() != 1 {
            bail!(
                "{label} routing control token {token:?} encoded to {} ids ({ids:?}); expected exactly one",
                ids.len()
            );
        }
        Ok(ids[0])
    };

    if kind == mm_routing::image::ImagePromptKind::Nemotron {
        // Nemotron's fixed image controls are already registered as atomic
        // HF added tokens. Resolve them through the same ordinary `encode`
        // path used for the rendered request; unlike Kimi-K3, this layout has
        // no mixed trusted/untrusted segment that requires `encode_segments`.
        let resolve_atomic_control = |label: &str, token: &str| -> Result<TokenIdType> {
            let ids = tokenizer.encode(token)?.token_ids().to_vec();
            if ids.len() != 1 {
                bail!(
                    "{label} routing control token {token:?} encoded to {} ids ({ids:?}); expected exactly one",
                    ids.len()
                );
            }
            Ok(ids[0])
        };
        let image_context = resolve_atomic_control(
            "Nemotron image-context",
            mm_routing::nemotron::IMAGE_CONTEXT,
        )?;
        let expected_image_token_id =
            expected_image_token_id.context("Nemotron image context token id is unavailable")?;
        anyhow::ensure!(
            image_context == expected_image_token_id,
            "Nemotron tokenizer image-context id {image_context} does not match config id {expected_image_token_id}"
        );
        return Ok(RoutingImagePromptLayout::WrappedRepeatedPad {
            image_start: resolve_atomic_control(
                "Nemotron image-start",
                mm_routing::nemotron::IMAGE_START,
            )?,
            image_end: resolve_atomic_control(
                "Nemotron image-end",
                mm_routing::nemotron::IMAGE_END,
            )?,
        });
    }

    Ok(RoutingImagePromptLayout::KimiK3 {
        media_begin: resolve_segmented_control("Kimi-K3 media-begin", "<|media_begin|>")?,
        media_content: resolve_segmented_control("Kimi-K3 media-content", "<|media_content|>")?,
        media_end: resolve_segmented_control("Kimi-K3 media-end", "<|media_end|>")?,
    })
}

#[cfg(feature = "mm-routing")]
fn append_mm_routing_replacement(
    expanded: &mut Vec<TokenIdType>,
    tokenizer: &dyn Tokenizer,
    layout: RoutingImagePromptLayout,
    image: MmImageEntry,
    num_image_tokens: usize,
) -> Result<()> {
    let fill_token = dynamo_kv_router::protocols::pad_value_for_mm_hash(image.mm_hash);

    append_mm_routing_replacement_with_fill(
        expanded,
        tokenizer,
        layout,
        image,
        num_image_tokens,
        fill_token,
    )
}

#[cfg(feature = "mm-routing")]
fn append_mm_routing_replacement_with_fill(
    expanded: &mut Vec<TokenIdType>,
    tokenizer: &dyn Tokenizer,
    layout: RoutingImagePromptLayout,
    image: MmImageEntry,
    num_image_tokens: usize,
    fill_token: TokenIdType,
) -> Result<()> {
    match layout {
        RoutingImagePromptLayout::RepeatedPad => {
            expanded.extend(std::iter::repeat_n(fill_token, num_image_tokens));
        }
        RoutingImagePromptLayout::WrappedRepeatedPad {
            image_start,
            image_end,
        } => {
            expanded.push(image_start);
            expanded.extend(std::iter::repeat_n(fill_token, num_image_tokens));
            expanded.push(image_end);
        }
        RoutingImagePromptLayout::KimiK3 {
            media_begin,
            media_content,
            media_end,
        } => {
            expanded.push(media_begin);
            let dimensions = format!("image {}x{}", image.width, image.height);
            expanded.extend(encode_routing_segment(tokenizer, &dimensions, false)?);
            expanded.push(media_content);
            expanded.extend(std::iter::repeat_n(fill_token, num_image_tokens));
            expanded.push(media_end);
        }
    }
    Ok(())
}

/// Build the request-side hash representation that mirrors vLLM's event
/// normalizer block by block.
///
/// Most blocks use canonical pad-value tokens. If a feature-span boundary
/// does not contain an exact ordered placeholder/object mapping, the worker's
/// KV-event identity contract determines whether to keep those canonical
/// tokens or fall back to worker tokens plus MM metadata.
#[cfg(feature = "mm-routing")]
fn apply_tracked_mm_replacements(
    routing_prepend_bos: Option<TokenIdType>,
    replacements: &[TrackedMmRoutingReplacement],
    token_ids: &[TokenIdType],
    block_size: usize,
    image_token_id: Option<TokenIdType>,
    video_token_id: Option<TokenIdType>,
    kv_event_mm_identity: mm_routing::KvEventMmIdentity,
) -> Result<(
    Vec<TokenIdType>,
    usize,
    Vec<Option<dynamo_kv_router::protocols::BlockExtraInfo>>,
)> {
    use dynamo_kv_router::protocols::{BlockExtraInfo, BlockMmObjectInfo};
    use dynamo_kv_router::zmq_wire::{normalize_mm_placeholder_runs, normalize_mm_token_runs};

    anyhow::ensure!(block_size > 0, "MM routing block size must be positive");
    anyhow::ensure!(
        replacements.iter().all(|replacement| {
            !replacement.target_tokens.is_empty()
                && replacement.worker_tokens.len() == replacement.routing_tokens.len()
        }),
        "tracked MM routing replacements must have a non-empty target and equal token lengths"
    );

    let replacement_tokens = replacements.iter().try_fold(0usize, |total, replacement| {
        total
            .checked_add(replacement.routing_tokens.len())
            .context("MM routing replacement capacity overflow")
    })?;
    let capacity = token_ids
        .len()
        .checked_add(replacement_tokens)
        .and_then(|value| value.checked_add(routing_prepend_bos.is_some() as usize))
        .context("MM routing token capacity overflow")?;
    let mut worker_tokens = Vec::with_capacity(capacity);
    let mut routing_tokens = Vec::with_capacity(capacity);
    if let Some(bos) = routing_prepend_bos {
        worker_tokens.push(bos);
        routing_tokens.push(bos);
    }

    // Placeholder targets are model/modality contracts, so many media objects
    // normally share the same target. Deduplicate them once to keep the prompt
    // scan proportional to the number of modalities rather than media objects.
    let mut distinct_targets: Vec<&[TokenIdType]> = Vec::new();
    for replacement in replacements {
        let target = replacement.target_tokens.as_slice();
        if !distinct_targets.contains(&target) {
            distinct_targets.push(target);
        }
    }
    let target_matches_at = |target: &[TokenIdType], token_index: usize| {
        token_ids.get(token_index..token_index.saturating_add(target.len())) == Some(target)
    };

    let mut spans = Vec::with_capacity(replacements.len());
    let mut replacement_index = 0usize;
    let mut token_index = 0usize;
    while token_index < token_ids.len() {
        if let Some(replacement) = replacements.get(replacement_index)
            && target_matches_at(&replacement.target_tokens, token_index)
        {
            let start = worker_tokens.len();
            worker_tokens.extend_from_slice(&replacement.worker_tokens);
            routing_tokens.extend_from_slice(&replacement.routing_tokens);
            spans.push((start, worker_tokens.len(), replacement.mm_hash));
            token_index += replacement.target_tokens.len();
            replacement_index += 1;
            continue;
        }

        anyhow::ensure!(
            !distinct_targets
                .iter()
                .any(|target| target_matches_at(target, token_index)),
            "multimodal placeholders do not match request order"
        );
        worker_tokens.push(token_ids[token_index]);
        routing_tokens.push(token_ids[token_index]);
        token_index += 1;
    }

    anyhow::ensure!(
        replacement_index == replacements.len(),
        "tokenized prompt is missing multimodal placeholders"
    );
    let expanded_prompt_len = routing_tokens.len();
    let padded_len = expanded_prompt_len.div_ceil(block_size) * block_size;
    worker_tokens.resize(padded_len, 0);
    routing_tokens.resize(padded_len, 0);

    let mut block_mm_infos = vec![None; padded_len / block_size];
    for (block_index, block_start) in (0..padded_len).step_by(block_size).enumerate() {
        let block_end = block_start + block_size;
        let mm_hashes: Vec<u64> = spans
            .iter()
            .filter(|(start, end, _)| *start < block_end && *end > block_start)
            .map(|(_, _, mm_hash)| *mm_hash)
            .collect();
        if mm_hashes.is_empty() {
            continue;
        }

        let worker_block = &worker_tokens[block_start..block_end];
        let routing_block = &mut routing_tokens[block_start..block_end];
        let normalized = if video_token_id.is_some() {
            normalize_mm_placeholder_runs(worker_block, image_token_id, video_token_id, &mm_hashes)
                .map(|(tokens, _)| tokens)
        } else if let Some(image_token_id) = image_token_id
            && worker_block.contains(&image_token_id)
        {
            // This mirrors the legacy worker event path used by models such
            // as Nemotron, whose image and video embeddings share <image>.
            normalize_mm_token_runs(worker_block, image_token_id, &mm_hashes)
                .map(|(tokens, _)| tokens)
        } else {
            None
        };
        match normalized {
            Some(normalized) => {
                anyhow::ensure!(
                    normalized == routing_block,
                    "frontend MM replacement differs from KV-event normalization"
                );
            }
            None if kv_event_mm_identity == mm_routing::KvEventMmIdentity::PadValueTokens => {
                // SGLang has already replaced every placeholder with the
                // canonical media pad before publishing this KV-event block.
                // The request-side routing block is therefore complete as-is.
            }
            None => {
                routing_block.copy_from_slice(worker_block);
                block_mm_infos[block_index] = Some(BlockExtraInfo {
                    mm_objects: mm_hashes
                        .into_iter()
                        .map(|mm_hash| BlockMmObjectInfo {
                            mm_hash,
                            offsets: Vec::new(),
                        })
                        .collect(),
                });
            }
        }
    }

    Ok((routing_tokens, expanded_prompt_len, block_mm_infos))
}
/// Construct the unpadded routing sequence and return its exact logical
/// length. Errors are routing-only: callers must discard the partial vector
/// and fall back without failing the inference request.
#[cfg(feature = "mm-routing")]
fn expand_mm_routing_tokens(
    tokenizer: &dyn Tokenizer,
    prompt_layout: RoutingImagePromptLayout,
    routing_prepend_bos: Option<TokenIdType>,
    find_token_id: TokenIdType,
    mm_image_entries: &[MmImageEntry],
    n_tokens: &[usize],
    token_ids: &[TokenIdType],
) -> Result<(Vec<TokenIdType>, usize)> {
    debug_assert_eq!(mm_image_entries.len(), n_tokens.len());
    let n_total: usize = n_tokens.iter().sum();
    let bos_extra = routing_prepend_bos.is_some() as usize;
    let mut expanded = Vec::with_capacity(token_ids.len() + n_total + bos_extra);
    if let Some(bos) = routing_prepend_bos {
        expanded.push(bos);
    }

    let mut image_idx = 0usize;
    for &token_id in token_ids {
        if token_id == find_token_id && image_idx < mm_image_entries.len() {
            append_mm_routing_replacement(
                &mut expanded,
                tokenizer,
                prompt_layout,
                mm_image_entries[image_idx],
                n_tokens[image_idx],
            )?;
            image_idx += 1;
        } else {
            expanded.push(token_id);
        }
    }

    let expanded_prompt_len = expanded.len();
    Ok((expanded, expanded_prompt_len))
}

#[cfg(feature = "mm-routing")]
#[allow(clippy::too_many_arguments)]
fn try_expand_mm_routing_tokens(
    tokenizer: &dyn Tokenizer,
    prompt_layout: RoutingImagePromptLayout,
    routing_prepend_bos: Option<TokenIdType>,
    find_token_id: TokenIdType,
    mm_image_entries: &[MmImageEntry],
    n_tokens: &[usize],
    token_ids: &[TokenIdType],
    model_id: &str,
) -> Option<(Vec<TokenIdType>, usize)> {
    match expand_mm_routing_tokens(
        tokenizer,
        prompt_layout,
        routing_prepend_bos,
        find_token_id,
        mm_image_entries,
        n_tokens,
        token_ids,
    ) {
        Ok(expanded) => Some(expanded),
        Err(error) => {
            tracing::warn!(
                target: "mm_routing",
                model = model_id,
                %error,
                "routing-only image prompt expansion failed; skipping MM routing info"
            );
            None
        }
    }
}

struct MediaFetchTask<'a> {
    modality: &'static str,
    slot_idx: usize,
    content_part: Cow<'a, ChatCompletionRequestUserMessageContentPart>,
}

/// Per-request media content-part counts, carried to the metrics annotation.
/// Derived from `multi_modal_data`, so independent of the `mm-routing` feature.
#[derive(Debug, Clone, Copy, Default)]
pub struct MultimodalCounts {
    pub image: usize,
    pub video: usize,
    pub audio: usize,
}

impl MultimodalCounts {
    /// Count `image_url` / `video_url` / `audio_url` parts (vec length per modality).
    fn from_preprocessed(request: &PreprocessedRequest) -> Self {
        let count = |key: &str| {
            request
                .multi_modal_data
                .as_ref()
                .and_then(|m| m.get(key))
                .map_or(0, |v| v.len())
        };
        Self {
            image: count("image_url"),
            video: count("video_url"),
            audio: count("audio_url"),
        }
    }
}

#[cfg(feature = "mm-routing")]
fn checked_add_image_tokens(total: Option<usize>, next: usize) -> Option<usize> {
    total?.checked_add(next)
}

#[cfg(feature = "mm-routing")]
fn has_mm_processor_override(value: Option<&serde_json::Value>) -> bool {
    value.is_some_and(|value| match value {
        serde_json::Value::Null => false,
        serde_json::Value::Object(map) => !map.is_empty(),
        // The schema expects an object. Fail closed for every other JSON shape
        // because its processor semantics are unknown here.
        _ => true,
    })
}

#[cfg(feature = "mm-routing")]
fn exact_mm_routing_preconditions_met(
    has_user_uuid: bool,
    resolved_image_count: usize,
    total_image_count: usize,
    has_processor_override: bool,
) -> bool {
    // Processor kwargs participate in backend feature hashing and can change
    // prompt expansion. Until the frontend reproduces those transformations,
    // exact routing must fail closed so router and worker cache keys agree.
    !has_user_uuid && !has_processor_override && resolved_image_count == total_image_count
}

#[cfg(all(feature = "mm-routing", feature = "media-ffmpeg"))]
fn should_hash_decoded_video(
    exact_mm_routing_eligible: bool,
    has_user_uuid: bool,
    has_processor_override: bool,
    has_video_routing_processor: bool,
) -> bool {
    exact_mm_routing_eligible
        && !has_user_uuid
        && !has_processor_override
        && has_video_routing_processor
}

#[cfg(feature = "mm-routing")]
fn exact_mm_routing_supports_modality(modality: &str, frontend_decoding: bool) -> bool {
    modality == "image_url" || (modality == "video_url" && frontend_decoding)
}

#[cfg(feature = "mm-routing")]
fn aggregate_image_tokens(
    image_tokens: Option<usize>,
    resolved_image_count: usize,
    total_image_count: usize,
    has_processor_override: bool,
) -> Option<usize> {
    if total_image_count == 0 || has_processor_override || resolved_image_count != total_image_count
    {
        return None;
    }

    image_tokens
}

/// Derive the model's local directory from the MDC. The directory is the
/// parent of `config.json` (which lives in `mdc.model_info` as `HfConfigJson`)
/// and contains the other artifacts MM-aware routing reads at startup
/// (`tokenizer.json`, `processor_config.json`, `preprocessor_config.json`).
/// Returns `None` for cards built from non-disk sources.
#[cfg(feature = "mm-routing")]
fn mdc_model_dir(mdc: &ModelDeploymentCard) -> Option<std::path::PathBuf> {
    let ModelInfoType::HfConfigJson(cf) = mdc.model_info.as_ref()?;
    cf.path()?.parent().map(std::path::PathBuf::from)
}

/// Shared SSRF-aware `MediaFetcher` + `reqwest::Client` for the dim-fetch
/// path used by MM-aware routing and image-token metrics. Inherits the same
/// policy contract as the frontend-decode path (`MediaLoader`): blocklist DNS
/// resolver, redirect revalidation, hostname/IP blocklist,
/// `DYN_MM_ALLOW_INTERNAL` opt-in.
///
/// **Lifecycle:** `LazyLock` so the closure runs on first access. For MM-
/// countable or routable preprocessors, `OpenAIPreprocessor::new_with_parts` calls
/// `LazyLock::force(...)` at startup — that surfaces TLS-root / reqwest-
/// init / env-misconfig failures at deployment time, not on the first MM
/// request 20 minutes in. Text-only deployments skip the force, leaving
/// the LazyLock dormant.
#[cfg(feature = "mm-routing")]
static DIM_FETCH_MEDIA_FETCHER: std::sync::LazyLock<crate::preprocessor::media::MediaFetcher> =
    std::sync::LazyLock::new(crate::preprocessor::media::MediaFetcher::from_env);

#[cfg(feature = "mm-routing")]
static DIM_FETCH_HTTP_CLIENT: std::sync::LazyLock<reqwest::Client> =
    std::sync::LazyLock::new(|| {
        DIM_FETCH_MEDIA_FETCHER
            .build_http_client()
            .expect("dim-fetch http client construction failed")
    });

pub(crate) const PRESERVE_OMITTED_MAX_TOKENS_CONTEXT_KEY: &str =
    "dynamo.llm.preserve_omitted_max_tokens";
pub(crate) const REQUEST_PARSING_OPTIONS_CONTEXT_KEY: &str = "dynamo.llm.request_parsing_options";

const EMBEDDING_ADD_SPECIAL_TOKENS_ENV: &str = "DYN_EMBEDDING_TOKENIZATION_ADD_SPECIAL_TOKENS";

fn parse_embedding_add_special_tokens(value: &str) -> Option<bool> {
    parse_bool_opt(value)
}

/// Run CPU-bound preprocessing off the async runtime. On the multi-thread runtime
/// `block_in_place` hands this worker to the blocking pool, so the I/O tasks that
/// share the runtime keep draining sockets while a long conversation renders;
/// under load a synchronous render here can leave request bodies unread for
/// tens of seconds. Falls back to inline on the current-thread runtime, where
/// `block_in_place` is not available.
///
/// Tokenization already does this via `spawn_blocking` (see `encode_with_timing`);
/// the template render was the remaining synchronous step on the request path.
fn off_runtime<T>(f: impl FnOnce() -> T) -> T {
    match tokio::runtime::Handle::try_current().map(|h| h.runtime_flavor()) {
        Ok(tokio::runtime::RuntimeFlavor::MultiThread) => tokio::task::block_in_place(f),
        _ => f(),
    }
}

fn embedding_add_special_tokens_env() -> Result<Option<bool>> {
    match std::env::var(EMBEDDING_ADD_SPECIAL_TOKENS_ENV) {
        Ok(value) => match parse_embedding_add_special_tokens(&value) {
            Some(value) => Ok(Some(value)),
            None => bail!(
                "invalid value {value:?} for {EMBEDDING_ADD_SPECIAL_TOKENS_ENV}; \
                 expected true/false/on/off/yes/no/1/0"
            ),
        },
        Err(std::env::VarError::NotPresent) => Ok(None),
        Err(std::env::VarError::NotUnicode(_)) => bail!(
            "{EMBEDDING_ADD_SPECIAL_TOKENS_ENV} must be valid Unicode and one of \
             true/false/on/off/yes/no/1/0"
        ),
    }
}

#[cfg(test)]
mod embedding_add_special_tokens_env_tests {
    use super::parse_embedding_add_special_tokens;

    #[test]
    fn parser_uses_the_documented_truth_table() {
        for value in ["1", "true", "on", "yes", " TRUE ", "On", "Yes"] {
            assert_eq!(parse_embedding_add_special_tokens(value), Some(true));
        }
        for value in ["0", "false", "off", "no", " FALSE ", "Off", "No"] {
            assert_eq!(parse_embedding_add_special_tokens(value), Some(false));
        }
        for value in ["", "yes-please"] {
            assert_eq!(parse_embedding_add_special_tokens(value), None);
        }
    }
}

fn embedding_chat_template_present(mdc: &ModelDeploymentCard) -> Result<bool> {
    if mdc.chat_template_file.is_some() {
        return Ok(true);
    }

    let Some(artifact) = mdc.prompt_formatter.as_ref() else {
        return Ok(false);
    };
    let PromptFormatterArtifact::HfTokenizerConfigJson(checked_file) = artifact else {
        return Ok(true);
    };
    let Some(path) = checked_file.path() else {
        return Ok(true);
    };

    let contents = std::fs::read_to_string(path)
        .with_context(|| format!("fs:read_to_string '{}'", path.display()))?;
    let config: serde_json::Value = serde_json::from_str(&contents)
        .with_context(|| format!("Failed to parse '{}'", path.display()))?;
    Ok(config
        .get("chat_template")
        .is_some_and(|template| !template.is_null()))
}

fn embedding_prompt_formatter(mdc: &ModelDeploymentCard) -> Result<PromptFormatter> {
    match prompt_formatter_from_mdc(mdc) {
        Ok(formatter) => Ok(formatter),
        Err(_) if !embedding_chat_template_present(mdc)? => Ok(PromptFormatter::no_op()),
        Err(err) => Err(err),
    }
}

fn attach_agent_context_from_context(
    request: &mut PreprocessedRequest,
    context: &PipelineContext<()>,
) {
    if let Ok(agent_context) = context.get::<crate::protocols::common::extensions::AgentContext>(
        crate::protocols::common::extensions::AGENT_CONTEXT_CONTEXT_KEY,
    ) {
        request.agent_context = Some(agent_context.as_ref().clone());
    }
}

fn attach_image_cache_scope_from_context(
    request: &mut PreprocessedRequest,
    context: &PipelineContext<()>,
) {
    use crate::protocols::common::extensions::{SESSION_AFFINITY_CONTEXT_KEY, SessionAffinityId};

    if let Ok(session_affinity) = context.get::<SessionAffinityId>(SESSION_AFFINITY_CONTEXT_KEY) {
        request.image_cache_scope = Some(session_affinity.as_str().to_owned());
    }
}

fn attach_request_context_metadata(
    request: &mut PreprocessedRequest,
    context: &PipelineContext<()>,
) {
    attach_agent_context_from_context(request, context);
    attach_image_cache_scope_from_context(request, context);
}

/// Thin wrapper that prepares messages for MiniJinja. Normalizes historical
/// `function.arguments` when the model opts in (GLM-5.2), and appends
/// HuggingFace's unique continue-final-message marker when that flag is set.
/// Other trait methods delegate to the inner request, except typed_messages:
/// rendering must use the transformed messages rather than the original slice.
struct NormalizedArgsRequest<'a, R> {
    inner: &'a R,
    normalize_tool_call_args: bool,
    continue_final_message: bool,
}

impl<R: OAIChatLikeRequest> OAIChatLikeRequest for NormalizedArgsRequest<'_, R> {
    fn model(&self) -> String {
        self.inner.model()
    }

    fn messages(&self) -> minijinja::value::Value {
        let mut json = serde_json::to_value(self.inner.typed_messages().unwrap_or_default())
            .unwrap_or_default();
        if self.normalize_tool_call_args
            && let Err(e) = crate::preprocessor::prompt::normalize_tool_call_arguments(&mut json)
        {
            tracing::error!(
                error = %e,
                "tool_call arguments normalization failed; template rendering may fail \
                 if it calls .items() on a string"
            );
        }
        if self.continue_final_message
            && let Err(e) =
                crate::preprocessor::prompt::append_continue_final_message_tag(&mut json)
        {
            tracing::debug!(
                error = %e,
                "continue_final_message marker not appended; truncation will reject the request"
            );
        }
        minijinja::value::Value::from_serialize(&json)
    }

    fn tools(&self) -> Option<minijinja::value::Value> {
        self.inner.tools()
    }

    fn tool_choice(&self) -> Option<minijinja::value::Value> {
        self.inner.tool_choice()
    }

    fn response_format(&self) -> Option<minijinja::value::Value> {
        self.inner.response_format()
    }

    fn should_add_generation_prompt(&self) -> bool {
        self.inner.should_add_generation_prompt()
    }

    fn extract_text(&self) -> Option<TextInput> {
        self.inner.extract_text()
    }

    fn chat_template_args(&self) -> Option<&std::collections::HashMap<String, serde_json::Value>> {
        self.inner.chat_template_args()
    }

    fn mm_processor_kwargs(&self) -> Option<&serde_json::Value> {
        self.inner.mm_processor_kwargs()
    }
}

impl<R: AnnotationsProvider> AnnotationsProvider for NormalizedArgsRequest<'_, R> {
    fn annotations(&self) -> Option<Vec<String>> {
        self.inner.annotations()
    }
}

impl<R: SamplingOptionsProvider> SamplingOptionsProvider for NormalizedArgsRequest<'_, R> {
    fn extract_sampling_options(
        &self,
    ) -> anyhow::Result<crate::protocols::common::SamplingOptions> {
        self.inner.extract_sampling_options()
    }
}

impl<R: StopConditionsProvider> StopConditionsProvider for NormalizedArgsRequest<'_, R> {
    fn extract_stop_conditions(&self) -> anyhow::Result<crate::protocols::common::StopConditions> {
        self.inner.extract_stop_conditions()
    }
}

impl<R: OutputOptionsProvider> OutputOptionsProvider for NormalizedArgsRequest<'_, R> {
    fn extract_output_options(&self) -> anyhow::Result<crate::protocols::common::OutputOptions> {
        self.inner.extract_output_options()
    }
}

impl<R: NvExtProvider> NvExtProvider for NormalizedArgsRequest<'_, R> {
    fn nvext(&self) -> Option<&crate::protocols::common::extensions::NvExt> {
        self.inner.nvext()
    }

    fn raw_prompt(&self) -> Option<String> {
        self.inner.raw_prompt()
    }

    fn unsupported_fields(&self) -> Option<&std::collections::HashMap<String, serde_json::Value>> {
        self.inner.unsupported_fields()
    }
}

#[derive(Clone, Copy, Debug, Default)]
struct PreprocessRequestOptions {
    preserve_omitted_max_tokens: bool,
}

struct EmbeddingTokenizerState {
    model_card: ModelDeploymentCard,
    with_special_tokens: OnceLock<Arc<dyn Tokenizer>>,
    without_special_tokens: OnceLock<Arc<dyn Tokenizer>>,
    initialization_lock: Mutex<()>,
    add_special_tokens_default: Option<bool>,
}

impl EmbeddingTokenizerState {
    fn new(model_card: &ModelDeploymentCard) -> Result<Self> {
        Ok(Self {
            model_card: model_card.clone(),
            with_special_tokens: OnceLock::new(),
            without_special_tokens: OnceLock::new(),
            initialization_lock: Mutex::new(()),
            add_special_tokens_default: embedding_add_special_tokens_env()?,
        })
    }

    fn tokenizer(&self, add_special_tokens: bool) -> Result<Arc<dyn Tokenizer>> {
        let tokenizer = if add_special_tokens {
            &self.with_special_tokens
        } else {
            &self.without_special_tokens
        };
        if let Some(tokenizer) = tokenizer.get() {
            return Ok(tokenizer.clone());
        }

        let _guard = self
            .initialization_lock
            .lock()
            .map_err(|_| anyhow::anyhow!("embedding tokenizer initialization lock was poisoned"))?;
        if let Some(tokenizer) = tokenizer.get() {
            return Ok(tokenizer.clone());
        }

        let initialized = self.model_card.embedding_tokenizer_with_options(
            crate::tokenizers::TokenizerOptions { add_special_tokens },
        )?;
        let initialized: Arc<dyn Tokenizer> = (*initialized).clone();
        Ok(tokenizer.get_or_init(|| initialized).clone())
    }
}

pub struct OpenAIPreprocessor {
    mdcsum: String,
    formatter: Arc<dyn OAIPromptFormatter>,
    tokenizer: Arc<dyn Tokenizer>,
    /// Present only for token-input embedding pipelines. The two tokenizer
    /// variants are initialized independently on demand.
    embedding_tokenizers: Option<EmbeddingTokenizerState>,
    model_info: Arc<dyn ModelInfo>,
    /// Exclusive bound for client token ids (`nvext.token_data`, token
    /// prompts, and embedding token input), from `token_id_bound`.
    token_id_bound: Option<usize>,
    lora_name: Option<String>,
    /// Per-model runtime configuration propagated to response generator (e.g., reasoning/tool parser)
    runtime_config: crate::local_model::runtime_config::ModelRuntimeConfig,
    structural_tag_reasoning_boundary: structural_tag::ResolvedReasoningBoundary,
    /// KV cache block size published in the model deployment card.
    kv_cache_block_size: usize,
    tool_call_parser: Option<String>,
    // Resolved at model setup so requests do not construct parsers to inspect their grammar.
    parser_requires_special_tokens: bool,
    /// Normalize historical tool-call `function.arguments` from a JSON string
    /// to an object before MiniJinja rendering.  Enabled for GLM-5.2 (glm47
    /// parser) and any model that sets `normalize_tool_call_args: true` in its
    /// ModelRuntimeConfig; disabled for all other models.
    normalize_tool_call_args: bool,
    media_loader: Option<MediaLoader>,
    /// Engine-published request-token admission policy.
    token_budget: Option<TokenBudget>,
    /// Model context limit used by the embedding truncation contract.
    context_length: u32,
    /// Tracks warmups and cancels them when this preprocessor is retired.
    speculative_prefill_tasks: speculative_prefill::PrefillTasks,
    /// Per-image token-count engine. `None` when the feature is disabled, the
    /// model isn't covered by the registry, or `preprocessor_config.json` is
    /// unreadable.
    #[cfg(feature = "mm-routing")]
    image_token_counter: Option<mm_routing::image::ImageRoutingProcessor>,
    /// Lightweight model-visible video expansion. Unlike the image counter,
    /// this does not resize or normalize pixels in the frontend.
    #[cfg(all(feature = "mm-routing", feature = "media-ffmpeg"))]
    video_routing_processor: Option<mm_routing::VideoRoutingProcessor>,
    /// Image-placeholder token id the routing-side sequence fills per image.
    /// Resolved from `config.json`'s `image_token_id` field when present,
    /// otherwise falls back to the `ModelProcessorSpec` registry value. This
    /// is the id the backend's HF processor emits in the expanded sequence
    /// (per-patch token for Qwen-VL families, the single placeholder for
    /// LLaVA), so block hashes align bit-for-bit with the worker.
    ///
    /// `None` disables MM-aware routing for this model and the router falls
    /// back to text-prefix routing.
    #[cfg(feature = "mm-routing")]
    routing_image_token_id: Option<crate::protocols::TokenIdType>,
    /// Model-specific routing-side image prompt shape. Most families replace
    /// only an existing pad token; Kimi-K3 inserts its structural/dimension
    /// block, while Nemotron wraps its repeated image tokens in delimiters.
    /// `None` disables exact MM routing.
    #[cfg(feature = "mm-routing")]
    routing_image_prompt_layout: Option<RoutingImagePromptLayout>,
    /// Dimension semantics used by URL-passthrough routing. Kimi-K3's vLLM
    /// processor applies EXIF transpose before rendering its dimension block.
    #[cfg(feature = "mm-routing")]
    routing_image_dimension_policy: RoutingImageDimensionPolicy,
    /// BOS token id to prepend to the routing-side sequence so per-block
    /// hashes match the backend's HF processor output on models with
    /// `add_bos_token: true` (LLaVA-1.5 and other `LlamaTokenizer`
    /// families). Applied only to requests containing images; video-only
    /// routing starts from the frontend-tokenized prompt. `None` when the
    /// model doesn't need it or `bos_token` doesn't round-trip to one id.
    #[cfg(feature = "mm-routing")]
    routing_prepend_bos: Option<crate::protocols::TokenIdType>,
}

pub(crate) const LORA_NAME_CONTEXT_KEY: &str = "discovery.lora_name";

/// Exclusive bound for client token ids: the larger of the model's vocab size
/// and the tokenizer's largest id plus one, so ids that only the model or only
/// the tokenizer has (e.g. an image placeholder) stay valid. A zero counts as
/// unknown. `None` means no check.
fn token_id_bound(model_vocab: Option<usize>, tokenizer_bound: Option<usize>) -> Option<usize> {
    model_vocab
        .into_iter()
        .chain(tokenizer_bound)
        .filter(|&size| size > 0)
        .max()
}

/// Reject token ids `>= bound` (client 400). `None` means no check.
/// `field` names the request field in the error.
fn ensure_token_ids_in_vocab(
    field: &str,
    tokens: &[crate::protocols::TokenIdType],
    bound: Option<usize>,
) -> anyhow::Result<()> {
    if let Some(bound) = bound
        && let Some(&bad) = tokens.iter().find(|&&t| t as usize >= bound)
    {
        return Err(invalid_argument_error(format!(
            "{field} token id {bad} is out of range (must be < {bound})"
        )));
    }
    Ok(())
}

#[cfg(all(feature = "mm-routing", feature = "media-ffmpeg"))]
fn resolve_qwen_video_processor_contract(
    runtime_config: &ModelRuntimeConfig,
) -> Result<Option<mm_routing::QwenVideoProcessorContract>> {
    let vllm_contract = runtime_config
        .get_engine_specific::<mm_routing::VllmQwenVideoProcessorContract>(
            VLLM_QWEN_VIDEO_PROCESSOR_CONTRACT_RUNTIME_KEY,
        )
        .with_context(|| {
            format!(
                "invalid Qwen video processor runtime metadata under {VLLM_QWEN_VIDEO_PROCESSOR_CONTRACT_RUNTIME_KEY}"
            )
        })?
        .map(mm_routing::QwenVideoProcessorContract::try_from)
        .transpose()?;
    let sglang_contract = runtime_config
        .get_engine_specific::<mm_routing::SglangQwenVideoProcessorContract>(
            SGLANG_QWEN_VIDEO_PROCESSOR_CONTRACT_RUNTIME_KEY,
        )
        .with_context(|| {
            format!(
                "invalid Qwen video processor runtime metadata under {SGLANG_QWEN_VIDEO_PROCESSOR_CONTRACT_RUNTIME_KEY}"
            )
        })?
        .map(mm_routing::QwenVideoProcessorContract::try_from)
        .transpose()?;

    anyhow::ensure!(
        vllm_contract.is_none() || sglang_contract.is_none(),
        "multiple Qwen video processor contracts were published"
    );
    Ok(vllm_contract.or(sglang_contract))
}

impl OpenAIPreprocessor {
    fn omitted_max_tokens_default(
        prompt_len: usize,
        token_limit: Option<u32>,
        options: PreprocessRequestOptions,
    ) -> Option<u32> {
        if options.preserve_omitted_max_tokens {
            return None;
        }
        token_limit.map(|limit| limit.saturating_sub(prompt_len as u32))
    }

    /// Return the exact prompt length when the frontend can prove it.
    ///
    /// MM routing currently expands image placeholders only. Therefore any
    /// other non-empty media modality makes even an image-expanded length
    /// incomplete and forces backend validation.
    fn exact_prompt_len(
        expanded_image_prompt_len: Option<usize>,
        multi_modal_data: Option<&MultimodalDataMap>,
        token_ids_len: usize,
    ) -> Option<usize> {
        let has_images = multi_modal_data
            .and_then(|media| media.get("image_url"))
            .is_some_and(|items| !items.is_empty());
        let has_other_media = multi_modal_data.is_some_and(|media| {
            media
                .iter()
                .any(|(kind, items)| kind != "image_url" && !items.is_empty())
        });

        if has_other_media {
            None
        } else if let Some(expanded_prompt_len) =
            expanded_image_prompt_len.filter(|length| *length > 0)
        {
            Some(expanded_prompt_len)
        } else if has_images {
            None
        } else {
            Some(token_ids_len)
        }
    }

    /// Apply the engine-published rejection policy to an exact request length.
    fn validate_requested_token_budget(
        prompt_len: usize,
        max_tokens: Option<u32>,
        token_budget: Option<&TokenBudget>,
    ) -> Result<()> {
        let Some(token_budget) = token_budget else {
            return Ok(());
        };
        let combined_limit = token_budget.combined_limit as usize;

        // A prompt that fills the combined budget leaves no room for generation.
        // When prompt overflow is backend-owned, its effective length may change.
        if prompt_len >= combined_limit {
            if !token_budget.reject_prompt_overflow {
                return Ok(());
            }
            return Err(Self::prompt_overflow_error(prompt_len, combined_limit).into());
        }

        // Generation requires at least one output token when the cap is omitted.
        let requested_tokens = prompt_len.saturating_add(max_tokens.unwrap_or(1) as usize);
        if requested_tokens > combined_limit && token_budget.reject_total_overflow {
            let request_description = match max_tokens {
                Some(max_tokens) => format!(
                    "your request has {prompt_len} input tokens and asks for {max_tokens} output \
                     tokens ({requested_tokens} tokens total)"
                ),
                None => format!(
                    "your request has {prompt_len} input tokens and requires room for at least one \
                     output token ({requested_tokens} tokens minimum)"
                ),
            };
            return Err(DynamoError::builder()
                .error_type(ErrorType::InvalidArgument)
                .message(format!(
                    "This model configuration accepts at most {} combined input and output \
                     tokens. However, {}. Please reduce the input length or requested output \
                     length.",
                    combined_limit, request_description,
                ))
                .public_details(PublicDetails::ContextLength {
                    limit: combined_limit as u64,
                    actual: Some(requested_tokens as u64),
                })
                .build()
                .into());
        }

        Ok(())
    }

    /// Validate a preprocessed request when its frontend-visible prompt length
    /// is exact, returning that length for other context-budget decisions.
    fn validate_preprocessed_token_budget(
        request: &PreprocessedRequest,
        token_budget: Option<&TokenBudget>,
    ) -> Result<Option<usize>> {
        if request.prompt_embeds.is_some() {
            return Ok(None);
        }

        let exact_prompt_len = Self::exact_prompt_len(
            request
                .mm_routing_info
                .as_ref()
                .map(|mm| mm.expanded_prompt_len),
            request.multi_modal_data.as_ref(),
            request.token_ids.len(),
        );
        if let Some(prompt_len) = exact_prompt_len {
            Self::validate_requested_token_budget(
                prompt_len,
                request.stop_conditions.max_tokens,
                token_budget,
            )?;
        }

        Ok(exact_prompt_len)
    }

    fn nvext_passthrough_args<R: NvExtProvider>(
        request: &R,
    ) -> Option<serde_json::Map<String, serde_json::Value>> {
        let mut nvext_passthrough = serde_json::Map::new();

        if let Some(nvext) = request.nvext() {
            if let Some(ref fields) = nvext.extra_fields {
                nvext_passthrough.insert("extra_fields".to_string(), serde_json::json!(fields));
            }
            if let Some(ref metadata_upload) = nvext.metadata_upload {
                nvext_passthrough.insert(
                    "metadata_upload".to_string(),
                    serde_json::json!(metadata_upload),
                );
            }
            if nvext.token_data.is_some() {
                nvext_passthrough.insert("token_in".to_string(), serde_json::Value::Bool(true));
            }
        }

        if let Some(salt) = request_cache_salt(request) {
            nvext_passthrough.insert("cache_salt".to_string(), serde_json::json!(salt));
        }

        if nvext_passthrough.is_empty() {
            None
        } else {
            Some(nvext_passthrough)
        }
    }

    fn sampling_passthrough_args<R: NvExtProvider>(
        request: &R,
    ) -> Option<serde_json::Map<String, serde_json::Value>> {
        let mut sampling_passthrough = serde_json::Map::new();

        if let Some(fields) = request.unsupported_fields() {
            for key in [
                "detokenize",
                "allowed_token_ids",
                "bad_words_token_ids",
                "logprob_token_ids",
            ] {
                if let Some(value) = fields.get(key) {
                    sampling_passthrough.insert(key.to_string(), value.clone());
                }
            }
        }

        if sampling_passthrough.is_empty() {
            None
        } else {
            Some(sampling_passthrough)
        }
    }

    fn backend_extra_args<R: OAIChatLikeRequest + NvExtProvider>(
        request: &R,
        reasoning_parser_configured: bool,
        reasoning_ended: Option<bool>,
    ) -> Option<serde_json::Value> {
        let mut extra_args = serde_json::Map::new();

        if let Some(nvext_passthrough) = Self::nvext_passthrough_args(request) {
            extra_args.insert(
                "nvext".to_string(),
                serde_json::Value::Object(nvext_passthrough),
            );
        }

        if let Some(sampling_passthrough) = Self::sampling_passthrough_args(request) {
            extra_args.insert(
                "sampling_options".to_string(),
                serde_json::Value::Object(sampling_passthrough),
            );
        }

        // vLLM constructs some native reasoning parsers per request. Forward
        // the accepted public template arguments through the internal request
        // so its guided-decoding gate observes the same thinking mode used to
        // render the prompt.
        if reasoning_parser_configured
            && let Some(chat_template_args) = request.chat_template_args()
        {
            extra_args.insert(
                "reasoning_parser_kwargs".to_string(),
                serde_json::json!({ "chat_template_kwargs": chat_template_args }),
            );
        }
        if reasoning_parser_configured && let Some(reasoning_ended) = reasoning_ended {
            extra_args.insert("reasoning_ended".to_string(), reasoning_ended.into());
        }

        if extra_args.is_empty() {
            None
        } else {
            Some(serde_json::Value::Object(extra_args))
        }
    }

    fn has_request_thinking_control(
        chat_template_args: Option<&HashMap<String, serde_json::Value>>,
    ) -> bool {
        chat_template_args.is_some_and(|args| {
            [
                "thinking",
                "enable_thinking",
                "thinking_mode",
                "reasoning_effort",
            ]
            .iter()
            .any(|key| args.contains_key(*key))
        })
    }

    fn request_has_client_thinking_control(request: &NvCreateChatCompletionRequest) -> bool {
        request.thinking.is_some()
            || Self::has_request_thinking_control(request.chat_template_args.as_ref())
    }

    fn apply_default_thinking_mode_from_runtime_config(
        runtime_config: &crate::local_model::runtime_config::ModelRuntimeConfig,
        request: &mut NvCreateChatCompletionRequest,
    ) {
        let default_mode = runtime_config
            .runtime_data
            .get(DEFAULT_THINKING_MODE_RUNTIME_KEY)
            .and_then(serde_json::Value::as_str);
        let from_client = Self::request_has_client_thinking_control(request);
        Self::apply_default_thinking_mode_value(
            default_mode,
            &mut request.chat_template_args,
            from_client,
        );
    }

    fn apply_default_thinking_mode_value(
        default_mode: Option<&str>,
        chat_template_args: &mut Option<HashMap<String, serde_json::Value>>,
        thinking_control_from_client: bool,
    ) {
        if thinking_control_from_client {
            return;
        }
        let Some(default_mode) = default_mode else {
            return;
        };
        let enabled = match default_mode {
            "enabled" => true,
            "disabled" => false,
            other => {
                tracing::warn!(
                    default_thinking_mode = other,
                    "Ignoring invalid runtime_config default_thinking_mode; expected 'enabled' or 'disabled'"
                );
                return;
            }
        };

        let args = chat_template_args.get_or_insert_with(HashMap::new);
        args.insert("thinking".to_string(), serde_json::Value::Bool(enabled));
        args.insert(
            "enable_thinking".to_string(),
            serde_json::Value::Bool(enabled),
        );
        args.insert(
            "thinking_mode".to_string(),
            serde_json::Value::String(if enabled { "enabled" } else { "disabled" }.to_string()),
        );
    }

    fn apply_default_thinking_mode(&self, request: &mut NvCreateChatCompletionRequest) {
        Self::apply_default_thinking_mode_from_runtime_config(&self.runtime_config, request);
    }

    fn guided_output_requires_reasoning<R: OAIChatLikeRequest>(
        request: &R,
        reasoning_parser: Option<&str>,
    ) -> bool {
        if reasoning_parser.is_none() {
            return false;
        }

        let is_guided_tool_choice = Self::has_guided_tool_choice(request);
        let is_structured_response = Self::has_structured_response_format(request);
        let structured_response_requires_reasoning = is_structured_response
            && Self::structured_response_supports_sglang_reasoning_gate(reasoning_parser);

        (is_guided_tool_choice || structured_response_requires_reasoning)
            && Self::sglang_effective_reasoning_enabled(
                reasoning_parser,
                request.chat_template_args(),
            )
    }

    fn request_requires_reasoning<R: OAIChatLikeRequest>(
        request: &R,
        reasoning_parser: Option<&str>,
        has_thinking_budget: bool,
    ) -> bool {
        Self::guided_output_requires_reasoning(request, reasoning_parser)
            || (has_thinking_budget
                && Self::sglang_effective_reasoning_enabled(
                    reasoning_parser,
                    request.chat_template_args(),
                ))
    }

    fn structured_response_supports_sglang_reasoning_gate(reasoning_parser: Option<&str>) -> bool {
        // GPT-OSS/Harmony must skip SGLang's `require_reasoning + json_schema`
        // path for structured output until upstream fixes malformed Harmony:
        // https://github.com/sgl-project/sglang/issues/31019
        // Tool calling still uses `require_reasoning`.
        !matches!(reasoning_parser, Some("gpt_oss"))
    }

    pub(crate) fn has_structured_response_format<R: OAIChatLikeRequest>(request: &R) -> bool {
        request.response_format().is_some_and(|format| {
            format
                .get_attr("type")
                .ok()
                .is_some_and(|kind| kind.as_str().is_some_and(|kind| kind != "text"))
        })
    }

    /// Match the rendered prompt's reasoning mode before selecting SGLang NativeGrammar.
    fn sglang_effective_reasoning_enabled(
        reasoning_parser: Option<&str>,
        chat_template_args: Option<&std::collections::HashMap<String, serde_json::Value>>,
    ) -> bool {
        let thinking_enabled = dynamo_renderer::thinking_bool_from_args(chat_template_args);

        match reasoning_parser {
            // These SGLang reasoners are enabled unless the request opts out.
            Some("qwen3" | "glm45" | "nemotron_nano" | "nemotron3" | "nemotron_v3") => {
                thinking_enabled != Some(false)
            }
            Some("kimi_k25" | "kimi_k3" | "kimi-k3") => thinking_enabled != Some(false),
            Some("minimax_m2") => {
                Self::deepseek_renderer_reasoning_enabled(chat_template_args, true)
            }

            // DeepSeek V3/V3.1 templates are opt-in. The native V3.2/V4
            // renderers default to thinking; all honor the same aliases.
            Some("deepseek_v3" | "deepseek_v3_1") => {
                Self::deepseek_renderer_reasoning_enabled(chat_template_args, false)
            }
            Some(
                "deepseek_v3_2" | "deepseek_v4" | "deepseek-v4" | "deepseekv4" | "deepseek_v41",
            ) => Self::deepseek_renderer_reasoning_enabled(chat_template_args, true),
            Some("gemma4" | "gemma-4") => thinking_enabled == Some(true),

            // SGLang's Mistral reasoner is active only for a concrete effort.
            Some("mistral") => Self::mistral_reasoning_enabled(chat_template_args),

            // MiniMax M3 defaults to adaptive reasoning unless disabled.
            Some("minimax_m3" | "minimax-m3") => chat_template_args
                .and_then(|args| args.get("thinking_mode"))
                .and_then(serde_json::Value::as_str)
                .is_none_or(|mode| mode != "disabled"),

            // These SGLang reasoners do not expose a per-request off mode.
            Some("deepseek_r1" | "step3" | "gpt_oss" | "kimi") => true,
            _ => false,
        }
    }

    /// Mirror dynamo-renderer's DeepSeek reasoning-toggle precedence.
    fn deepseek_renderer_reasoning_enabled(
        chat_template_args: Option<&std::collections::HashMap<String, serde_json::Value>>,
        default_enabled: bool,
    ) -> bool {
        if let Some(enabled) = dynamo_renderer::thinking_bool_from_args(chat_template_args) {
            return enabled;
        }
        if let Some(mode) = chat_template_args
            .and_then(|args| args.get("thinking_mode"))
            .and_then(serde_json::Value::as_str)
        {
            return match mode {
                "chat" => false,
                "thinking" => true,
                _ => default_enabled,
            };
        }
        default_enabled
    }

    #[cfg(test)]
    fn normalize_thinking_arg(
        request: &mut NvCreateChatCompletionRequest,
        reasoning_parser: Option<&str>,
        tool_call_parser: Option<&str>,
    ) {
        let thinking_control_from_client = Self::request_has_client_thinking_control(request);
        Self::normalize_thinking_arg_with_source(
            request,
            reasoning_parser,
            tool_call_parser,
            thinking_control_from_client,
        );
    }

    fn normalize_thinking_arg_with_source(
        request: &mut NvCreateChatCompletionRequest,
        reasoning_parser: Option<&str>,
        tool_call_parser: Option<&str>,
        thinking_control_from_client: bool,
    ) {
        let constrained_generation = !thinking_control_from_client
            && Self::is_minimax_m3_family(reasoning_parser, tool_call_parser)
            && Self::has_minimax_m3_constrained_generation(request);
        Self::normalize_thinking_args_with_source(
            &mut request.chat_template_args,
            reasoning_parser,
            tool_call_parser,
            thinking_control_from_client,
            constrained_generation,
        );
    }

    fn normalize_thinking_args_with_source(
        chat_template_args: &mut Option<HashMap<String, serde_json::Value>>,
        reasoning_parser: Option<&str>,
        tool_call_parser: Option<&str>,
        thinking_control_from_client: bool,
        constrained_generation: bool,
    ) {
        let normalized =
            Self::normalize_thinking_aliases(chat_template_args.as_ref(), reasoning_parser);

        if Self::is_minimax_m3_family(reasoning_parser, tool_call_parser) {
            Self::normalize_minimax_m3_thinking_mode(
                chat_template_args,
                normalized,
                thinking_control_from_client,
                constrained_generation,
            );
            return;
        }

        Self::apply_normalized_thinking_aliases(chat_template_args, normalized);
    }

    fn normalize_thinking_aliases(
        chat_template_args: Option<&HashMap<String, serde_json::Value>>,
        reasoning_parser: Option<&str>,
    ) -> Option<bool> {
        chat_template_args
            .and_then(|args| {
                // Normalize public aliases in array order: `thinking`,
                // `enable_thinking`, then `thinking_mode`.
                // Preserve "adaptive" as tri-state by returning None.
                for key in ["thinking", "enable_thinking", "thinking_mode"] {
                    match args.get(key) {
                        Some(serde_json::Value::Bool(b)) => {
                            // `thinking_bool_from_args` ignores `thinking_mode`,
                            // so read that bool directly.
                            if key == "thinking_mode" {
                                return Some(*b);
                            }
                            return dynamo_renderer::thinking_bool_from_args(Some(args));
                        }
                        Some(serde_json::Value::String(value)) => {
                            // Parse `thinking_mode` explicitly so
                            // "enabled"/"disabled" and boolean spellings
                            // behave predictably. Preserve unrecognized strings
                            // such as "adaptive" or DeepSeek's "chat"/"thinking".
                            if key == "thinking_mode" {
                                if value.eq_ignore_ascii_case("enabled") {
                                    return Some(true);
                                }
                                if value.eq_ignore_ascii_case("disabled") {
                                    return Some(false);
                                }
                                if let Some(b) = parse_bool_opt(value) {
                                    return Some(b);
                                }
                                return None;
                            }
                            return Some(is_truthy(value));
                        }
                        Some(serde_json::Value::Number(value)) => {
                            if let Some(value) = value.as_f64() {
                                return Some(value != 0.0);
                            }
                        }
                        _ => {}
                    }
                }
                None
            })
            .or_else(|| {
                matches!(reasoning_parser, Some("kimi_k25" | "kimi_k3" | "kimi-k3")).then_some(true)
            })
    }

    fn normalize_minimax_m3_thinking_mode(
        chat_template_args: &mut Option<HashMap<String, serde_json::Value>>,
        normalized: Option<bool>,
        thinking_control_from_client: bool,
        constrained_generation: bool,
    ) {
        // MiniMax M3 defaults `thinking_mode` to "adaptive", which breaks
        // constrained JSON/schema and forced-tool generation. If the client
        // did not set thinking controls, force "disabled" for those requests.
        let explicit_thinking_mode_is_adaptive = chat_template_args
            .as_ref()
            .and_then(|args| args.get("thinking_mode"))
            .and_then(|v| v.as_str())
            .is_some_and(|s| s.eq_ignore_ascii_case("adaptive"));
        if !thinking_control_from_client && constrained_generation {
            let args = chat_template_args.get_or_insert_default();
            args.insert(
                "thinking_mode".to_string(),
                serde_json::Value::String("disabled".to_string()),
            );
            args.insert("thinking".to_string(), serde_json::Value::Bool(false));
            args.insert(
                "enable_thinking".to_string(),
                serde_json::Value::Bool(false),
            );
            return;
        }

        Self::apply_normalized_thinking_aliases(chat_template_args, normalized);

        let Some(normalized) = normalized else {
            return;
        };

        // Canonicalize for M3 because its template only treats exact
        // "disabled" as off. Preserve explicit "adaptive" as client intent.
        if !explicit_thinking_mode_is_adaptive {
            let args = chat_template_args.get_or_insert_default();
            args.insert(
                "thinking_mode".to_string(),
                serde_json::Value::String(
                    if normalized { "enabled" } else { "disabled" }.to_string(),
                ),
            );
        }
    }

    fn apply_normalized_thinking_aliases(
        chat_template_args: &mut Option<HashMap<String, serde_json::Value>>,
        normalized: Option<bool>,
    ) {
        let Some(normalized) = normalized else {
            return;
        };
        let args = chat_template_args.get_or_insert_default();
        args.insert("thinking".to_string(), serde_json::Value::Bool(normalized));
        args.insert(
            "enable_thinking".to_string(),
            serde_json::Value::Bool(normalized),
        );
    }

    /// True when the deployment renders MiniMax M3's `thinking_mode` template.
    /// Either parser is sufficient, and tool-call aliases include `_nom`.
    fn is_minimax_m3_family(
        reasoning_parser: Option<&str>,
        tool_call_parser: Option<&str>,
    ) -> bool {
        matches!(reasoning_parser, Some("minimax_m3") | Some("minimax-m3"))
            || matches!(
                tool_call_parser,
                Some("minimax_m3")
                    | Some("minimax-m3")
                    | Some("minimax_m3_nom")
                    | Some("minimax-m3-nom")
            )
    }

    fn has_minimax_m3_constrained_generation<R: OAIChatLikeRequest>(request: &R) -> bool {
        Self::has_structured_response_format(request) || Self::has_guided_tool_choice(request)
    }

    /// True when `tool_choice` forces a tool call.
    /// Shared by guided-output reasoning checks and the M3 default.
    fn has_guided_tool_choice<R: OAIChatLikeRequest>(request: &R) -> bool {
        request.tool_choice().is_some_and(|tool_choice| {
            match tool_choice.as_str() {
                Some("required") => true,
                Some(_) => false,
                // The only supported non-string tool choice is a named function.
                None => true,
            }
        })
    }

    /// Apply Moonshot's named-tool exception for Kimi K3.
    ///
    /// The public K3 API treats a specified function as incompatible with
    /// thinking. Normalize that rule onto the request before prompt rendering
    /// so every downstream consumer observes the same effective mode:
    ///
    /// - the K3 renderer omits current and preserved thinking channels,
    /// - prompt-injected reasoning detection stays false,
    /// - backend reasoning-parser kwargs carry the disabled state, and
    /// - response postprocessing does not try to parse a reasoning stream.
    fn normalize_kimi_k3_named_tool_choice(
        request: &mut NvCreateChatCompletionRequest,
        tool_call_parser: Option<&str>,
    ) {
        let uses_kimi_k3_parser =
            tool_call_parser.is_some_and(|parser| matches!(parser, "kimi_k3" | "kimi-k3"));
        let is_named = matches!(
            request.inner.tool_choice.as_ref(),
            Some(ChatCompletionToolChoiceOption::Named(_))
        );
        if !uses_kimi_k3_parser || !is_named {
            return;
        }

        let args = request.chat_template_args.get_or_insert_default();
        args.insert("thinking".to_string(), serde_json::Value::Bool(false));
        args.insert(
            "enable_thinking".to_string(),
            serde_json::Value::Bool(false),
        );
    }

    fn mistral_reasoning_enabled(
        chat_template_args: Option<&std::collections::HashMap<String, serde_json::Value>>,
    ) -> bool {
        chat_template_args
            .and_then(|args| args.get("reasoning_effort"))
            .and_then(serde_json::Value::as_str)
            .is_some_and(|effort| effort != "none")
    }

    pub fn new(mdc: ModelDeploymentCard) -> Result<Arc<Self>> {
        let formatter = prompt_formatter_from_mdc(&mdc)?;
        let tokenizer = mdc.tokenizer()?;
        match formatter {
            PromptFormatter::OAI(formatter) => Self::new_with_parts(mdc, formatter, tokenizer),
        }
    }

    /// Build the preprocessor used by token-input embedding pipelines.
    pub fn new_for_embeddings(mdc: ModelDeploymentCard) -> Result<Arc<Self>> {
        if !mdc.model_type.supports_embedding() {
            anyhow::bail!("embedding preprocessor requires an embedding-capable model");
        }

        let tokenizer = mdc.tokenizer()?;
        let PromptFormatter::OAI(formatter) = embedding_prompt_formatter(&mdc)?;
        let embedding_tokenizers = EmbeddingTokenizerState::new(&mdc)?;
        Self::new_with_parts_inner(mdc, formatter, tokenizer, Some(embedding_tokenizers), None)
    }

    pub fn new_with_parts(
        mdc: ModelDeploymentCard,
        formatter: Arc<dyn OAIPromptFormatter>,
        tokenizer: crate::tokenizers::Tokenizer,
    ) -> Result<Arc<Self>> {
        Self::new_with_parts_and_cancel(mdc, formatter, tokenizer, None)
    }

    /// Builds a preprocessor with an optional speculative-prefill shutdown token.
    pub fn new_with_parts_and_cancel(
        mdc: ModelDeploymentCard,
        formatter: Arc<dyn OAIPromptFormatter>,
        tokenizer: crate::tokenizers::Tokenizer,
        speculative_prefill_cancel: Option<CancellationToken>,
    ) -> Result<Arc<Self>> {
        Self::new_with_parts_inner(mdc, formatter, tokenizer, None, speculative_prefill_cancel)
    }

    fn new_with_parts_inner(
        mdc: ModelDeploymentCard,
        formatter: Arc<dyn OAIPromptFormatter>,
        tokenizer: crate::tokenizers::Tokenizer,
        embedding_tokenizers: Option<EmbeddingTokenizerState>,
        speculative_prefill_cancel: Option<CancellationToken>,
    ) -> Result<Arc<Self>> {
        let mdcsum = mdc.mdcsum().to_string();
        let tokenizer: Arc<dyn Tokenizer> = (*tokenizer).clone();
        let lora_name = mdc.lora.as_ref().map(|l| l.name.clone());
        let Some(ref model_info) = mdc.model_info else {
            anyhow::bail!(
                "Blank ModelDeploymentCard cannot be used for pre-processing, no model_info"
            );
        };
        let model_info = model_info.get_model_info()?;
        // The tokenizer trait exposes no ids, so the card keeps the bound.
        let tokenizer_id_bound = mdc.tokenizer_id_bound().unwrap_or_else(|| {
            tracing::warn!(
                model = %mdc.display_name,
                "Tokenizer not loaded from this model card: only vocab_size bounds client token ids"
            );
            None
        });
        let token_id_bound = token_id_bound(model_info.vocab_size(), tokenizer_id_bound);
        let tool_call_parser = mdc.runtime_config.tool_call_parser.clone();
        if mdc.model_type.supports_chat() {
            crate::protocols::openai::chat_completions::tool_parser_v2::validate_parser_version(
                tool_call_parser.as_deref(),
                mdc.runtime_config.reasoning_parser.as_deref(),
            )?;
            tracing::info!(
                model = %mdc.display_name,
                parser_version = ?crate::protocols::openai::chat_completions::tool_parser_v2::selected_version()?,
                unified_family = ?crate::protocols::openai::chat_completions::unified_parser::selected_family(
                    tool_call_parser.as_deref(),
                    mdc.runtime_config.reasoning_parser.as_deref(),
                ),
                tool_parser = ?tool_call_parser,
                reasoning_parser = ?mdc.runtime_config.reasoning_parser,
                "Selected model parser route"
            );
        }
        let parser_requires_special_tokens = mdc.model_type.supports_chat()
            && Self::parser_requires_special_tokens(
                tool_call_parser.as_deref(),
                mdc.runtime_config.reasoning_parser.as_deref(),
            )?;
        let normalize_tool_call_args = mdc.runtime_config.tool_call_arguments_format
            == crate::local_model::runtime_config::ToolCallArgumentsFormat::JsonObject
            || mdc.runtime_config.tool_call_parser.as_deref() == Some("glm47");

        if let Some(ref lora_name) = lora_name {
            tracing::info!(model = %mdc.display_name, lora_name, "LoRA adapter detected in MDC");
        }

        // // Initialize runtime config from the ModelDeploymentCard
        let runtime_config = mdc.runtime_config.clone();
        let structural_tag_reasoning_boundary =
            structural_tag::validate_runtime_config(&runtime_config)?;
        let token_budget = match runtime_config
            .get_engine_specific::<TokenBudget>(TOKEN_BUDGET_RUNTIME_KEY)
        {
            Ok(token_budget) => token_budget,
            Err(error) => {
                tracing::warn!(
                    %error,
                    key = TOKEN_BUDGET_RUNTIME_KEY,
                    "Ignoring invalid runtime metadata; token overflow handling will be delegated to the backend"
                );
                None
            }
        };
        let kv_cache_block_size = mdc.kv_cache_block_size as usize;
        let context_length = mdc.effective_context_length();

        // Capture MM-routing inputs before mdc is partially moved into MediaLoader.
        // model_type comes from config.json (e.g. "qwen3_vl") and lets the
        // image-processor registry resolve fine-tunes loaded from
        // custom-named directories where the family substring isn't in the path.
        #[cfg(feature = "mm-routing")]
        let model_dir_for_routing: Option<std::path::PathBuf> = mdc_model_dir(&mdc);
        #[cfg(feature = "mm-routing")]
        let fastokens_active = runtime_config.effective_tokenizer_backend().is_fastokens();
        // TODO(mm-routing): fastokens lacks a special-token mutator, so it
        // can't merge tokenizer_config.json specials and would BPE-shatter
        // placeholders (e.g. Qwen2-VL `<|image_pad|>`). Disable MM-routing
        // token resolution here, but keep SMG image-token counting enabled.
        // Remove this split once fastokens upstream exposes the mutator.
        #[cfg(feature = "mm-routing")]
        if fastokens_active && model_dir_for_routing.is_some() {
            tracing::warn!(
                target: "mm_routing",
                "fastokens tokenizer backend is active; MM-aware KV routing disabled. \
                 Image-token metrics remain enabled when SMG supports the model."
            );
        }
        #[cfg(feature = "mm-routing")]
        let image_token_inputs: Option<(String, String, std::path::PathBuf)> =
            model_dir_for_routing.as_ref().map(|p| {
                (
                    mdc.source_path().to_string(),
                    model_info.model_type(),
                    p.clone(),
                )
            });

        let media_loader = match mdc.media_decoder {
            Some(media_decoder) => Some(MediaLoader::new(media_decoder, mdc.media_fetcher)?),
            None => None,
        };

        #[cfg(feature = "mm-routing")]
        let (
            image_token_counter,
            routing_image_token_id,
            routing_image_prompt_layout,
            bos_token_string,
        ) = match image_token_inputs.as_ref() {
            Some((model_id, model_type, model_dir)) => {
                // Resolve counter + image-token id independently so the
                // summary log can name which piece is missing.
                let (counter, counter_err): (
                    Option<mm_routing::image::ImageRoutingProcessor>,
                    Option<String>,
                ) = match mm_routing::image::ImageRoutingProcessor::try_new_for_runtime(
                    model_id,
                    Some(model_type),
                    model_dir,
                    if runtime_has_vllm_generate_capability(&runtime_config) {
                        mm_routing::image::ImageRoutingRuntime::VllmNativeGenerate
                    } else {
                        mm_routing::image::ImageRoutingRuntime::BackendNeutral
                    },
                ) {
                    Ok(c) => (Some(c), None),
                    Err(e) => (None, Some(e.to_string())),
                };
                let (img_tok, prompt_layout, bos_tok_string) = if fastokens_active {
                    (None, None, None)
                } else {
                    // One-shot config/tokenizer_config read for all
                    // routing-side token info. Parsing lives next to the
                    // spec resolution in the MM-routing module.
                    let routing_tokens = mm_routing::image::resolve_routing_tokens(
                        model_id,
                        model_dir,
                        counter.as_ref(),
                    );
                    let prompt_layout =
                            routing_tokens.image_prompt_kind.and_then(|kind| {
                                match resolve_routing_image_prompt_layout(
                                    tokenizer.as_ref(),
                                    kind,
                                    routing_tokens.chat_placeholder_token_id,
                                ) {
                                    Ok(layout) => Some(layout),
                                    Err(e) => {
                                        tracing::warn!(
                                            target: "mm_routing",
                                            model = %model_id,
                                            error = %e,
                                            "model-specific routing image prompt could not be resolved; exact MM routing disabled"
                                        );
                                        None
                                    }
                                }
                            });
                    // Exact routing is enabled only as an all-or-nothing
                    // bundle: counter, placeholder id, and prompt layout.
                    // The worker resolves the same static prerequisites; the
                    // frontend-issued MM UUID marks request-time readiness.
                    let exact_mm_routing_ready = counter.is_some() && prompt_layout.is_some();
                    (
                        routing_tokens.exact_routing_image_token_id(exact_mm_routing_ready),
                        prompt_layout.filter(|_| counter.is_some()),
                        routing_tokens.bos_token_string,
                    )
                };

                match (counter.is_some(), img_tok.is_some()) {
                    (true, true) => tracing::info!(
                        target: "mm_routing",
                        model = %model_id,
                        model_dir = %model_dir.display(),
                        "MM-aware KV routing enabled"
                    ),
                    _ if fastokens_active => {}
                    (counter_ok, img_ok) => {
                        let mut reasons: Vec<String> = Vec::new();
                        if !counter_ok {
                            reasons.push(format!(
                                "image routing processor unavailable ({})",
                                counter_err.as_deref().unwrap_or("unknown error")
                            ));
                        }
                        if !img_ok {
                            reasons.push(
                                "image-placeholder token or model-specific prompt layout \
                                     unresolvable from \
                                     config.json / processor_config.json / \
                                     tokenizer_config.json / vocab probe"
                                    .to_string(),
                            );
                        }
                        tracing::warn!(
                            target: "mm_routing",
                            model = %model_id,
                            reasons = %reasons.join("; "),
                            "{} is not supported for MM-aware KV routing ({}). \
                             Falling back to KV routing without MM awareness — \
                             text-prefix overlap still works but the router \
                             cannot distinguish requests by image content.",
                            model_id,
                            reasons.join("; ")
                        );
                    }
                }
                (counter, img_tok, prompt_layout, bos_tok_string)
            }
            None => {
                tracing::debug!(
                    target: "mm_routing",
                    "model directory not derivable from MDC; MM-aware routing disabled"
                );
                (None, None, None, None)
            }
        };

        #[cfg(all(feature = "mm-routing", feature = "media-ffmpeg"))]
        let video_routing_processor = {
            let qwen_contract = match resolve_qwen_video_processor_contract(&runtime_config) {
                Ok(contract) => contract,
                Err(error) => {
                    tracing::warn!(
                        target: "mm_routing",
                        %error,
                        "invalid Qwen video processor runtime metadata; exact video routing disabled"
                    );
                    None
                }
            };
            let nemotron_contract = match runtime_config
                .get_engine_specific::<mm_routing::NemotronVideoProcessorContract>(
                    VLLM_NEMOTRON_VIDEO_PROCESSOR_CONTRACT_RUNTIME_KEY,
                ) {
                Ok(target) => target,
                Err(error) => {
                    tracing::warn!(
                        target: "mm_routing",
                        %error,
                        key = VLLM_NEMOTRON_VIDEO_PROCESSOR_CONTRACT_RUNTIME_KEY,
                        "invalid Nemotron video processor runtime metadata; exact video routing disabled"
                    );
                    None
                }
            };
            let contracts = mm_routing::VideoProcessorContracts {
                qwen: qwen_contract,
                nemotron: nemotron_contract,
            };
            if fastokens_active {
                None
            } else {
                match (
                    image_token_inputs.as_ref(),
                    contracts.qwen.is_some() || contracts.nemotron.is_some(),
                ) {
                    (Some((model_id, model_type, model_dir)), true) => {
                        match mm_routing::VideoRoutingProcessor::try_new(
                            model_id,
                            model_type,
                            model_dir,
                            tokenizer.clone(),
                            contracts,
                        ) {
                            Ok(processor) => processor,
                            Err(error) => {
                                tracing::warn!(
                                    target: "mm_routing",
                                    model = %model_id,
                                    %error,
                                    "exact video-aware KV routing disabled for this model"
                                );
                                None
                            }
                        }
                    }
                    _ => None,
                }
            }
        };
        #[cfg(all(feature = "mm-routing", feature = "media-ffmpeg"))]
        if video_routing_processor.is_some()
            && let Some((model_id, model_type, _)) = image_token_inputs.as_ref()
        {
            tracing::info!(
                target: "mm_routing",
                model = %model_id,
                model_type = %model_type,
                "exact video-aware KV routing enabled"
            );
        }

        #[cfg(feature = "mm-routing")]
        let routing_image_dimension_policy = routing_image_dimension_policy(
            &runtime_config,
            media_loader.is_some(),
            routing_image_prompt_layout,
        );

        // Force the dim-fetch HTTP client to build at startup for any
        // MM-countable or routable preprocessor, so TLS / env-var / reqwest-init
        // failures fail the deployment instead of crashing the first
        // image request 20 minutes in. Other preprocessors skip the
        // force when both image-routing hooks resolve to `None` — no point
        // building a client they'll never use.
        #[cfg(feature = "mm-routing")]
        if image_token_counter.is_some() || routing_image_token_id.is_some() {
            std::sync::LazyLock::force(&DIM_FETCH_MEDIA_FETCHER);
            std::sync::LazyLock::force(&DIM_FETCH_HTTP_CLIENT);
        }

        // Resolve the routing-side BOS prepend for models with
        // `add_bos_token: true` (see `routing_prepend_bos` doc). Only kept
        // when the configured `bos_token` round-trips to a single id. The
        // BOS string was harvested above by `resolve_routing_tokens` from
        // the same `tokenizer_config.json` pass.
        #[cfg(feature = "mm-routing")]
        let routing_prepend_bos = match bos_token_string {
            Some(bos_text) => match tokenizer.encode(&bos_text) {
                Ok(enc) if enc.token_ids().len() == 1 => {
                    let id = enc.token_ids()[0];
                    tracing::debug!(
                        target: "mm_routing",
                        bos_token = %bos_text,
                        bos_token_id = id,
                        "routing-side BOS prepend enabled (tokenizer_config.json add_bos_token=true)"
                    );
                    Some(id)
                }
                Ok(enc) => {
                    tracing::debug!(
                        target: "mm_routing",
                        bos_token = %bos_text,
                        round_trip_ids = ?enc.token_ids(),
                        "BOS token does not round-trip to a single id; routing-side prepend disabled"
                    );
                    None
                }
                Err(e) => {
                    tracing::debug!(
                        target: "mm_routing",
                        bos_token = %bos_text,
                        error = %e,
                        "BOS token failed to re-encode; routing-side prepend disabled"
                    );
                    None
                }
            },
            None => None,
        };

        Ok(Arc::new(Self {
            formatter,
            tokenizer,
            embedding_tokenizers,
            model_info,
            token_id_bound,
            mdcsum,
            lora_name,
            runtime_config,
            structural_tag_reasoning_boundary,
            kv_cache_block_size,
            tool_call_parser,
            parser_requires_special_tokens,
            normalize_tool_call_args,
            media_loader,
            token_budget,
            context_length,
            speculative_prefill_tasks: speculative_prefill::PrefillTasks::new(
                speculative_prefill_cancel.as_ref(),
            ),
            #[cfg(feature = "mm-routing")]
            image_token_counter,
            #[cfg(all(feature = "mm-routing", feature = "media-ffmpeg"))]
            video_routing_processor,
            #[cfg(feature = "mm-routing")]
            routing_image_token_id,
            #[cfg(feature = "mm-routing")]
            routing_image_prompt_layout,
            #[cfg(feature = "mm-routing")]
            routing_image_dimension_policy,
            #[cfg(feature = "mm-routing")]
            routing_prepend_bos,
        }))
    }

    /// Encode a string to it's tokens
    pub fn tokenize(&self, s: &str) -> anyhow::Result<Encoding> {
        self.tokenizer.encode(s)
    }

    /// Encode a rendered prompt while preserving model-specific special-token
    /// boundaries.
    pub fn tokenize_rendered_prompt(&self, prompt: &RenderedPrompt) -> anyhow::Result<Encoding> {
        match prompt.encode_segments() {
            Some(segments) => self.tokenizer.encode_segments(&segments),
            None => self.tokenize(prompt.as_str()),
        }
    }

    /// Translate a [`NvCreateChatCompletionRequest`] request to a common completion request.
    /// Returns the common completion request, a hashmap of annotations, and a boolean
    /// indicating whether the rendered prompt ends with a reasoning start token (e.g.,
    /// `<think>`), meaning the model's completion will begin mid-reasoning.
    ///
    /// Annotations evaluated by this method include:
    /// - `formatted_prompt`
    /// - `token_ids`
    pub async fn preprocess_request<
        R: OAIChatLikeRequest
            + MediaRequestExt
            + AnnotationsProvider
            + SamplingOptionsProvider
            + StopConditionsProvider
            + OutputOptionsProvider
            + NvExtProvider
            + CommonExtProvider,
    >(
        &self,
        request: &R,
        tracker: Option<&RequestTracker>,
    ) -> Result<(
        PreprocessedRequest,
        HashMap<String, String>,
        PromptReasoningPrefill,
    )> {
        let (request, annotations, prompt_injected_reasoning, _image_tokens) = self
            .preprocess_request_with_options(
                request,
                tracker,
                PreprocessRequestOptions::default(),
                None,
            )
            .await?;
        Ok((request, annotations, prompt_injected_reasoning))
    }

    async fn preprocess_request_with_options<
        R: OAIChatLikeRequest
            + MediaRequestExt
            + AnnotationsProvider
            + SamplingOptionsProvider
            + StopConditionsProvider
            + OutputOptionsProvider
            + NvExtProvider
            + CommonExtProvider,
    >(
        &self,
        request: &R,
        tracker: Option<&RequestTracker>,
        options: PreprocessRequestOptions,
        lora_name: Option<String>,
    ) -> Result<(
        PreprocessedRequest,
        HashMap<String, String>,
        PromptReasoningPrefill,
        Option<usize>,
    )> {
        let _stage_guard = StageGuard::new(STAGE_PREPROCESS, "");
        let preprocess_start = Instant::now();
        let mut builder = self.builder_with_lora(request, lora_name)?;

        let template_start = Instant::now();
        let formatted_prompt = {
            let _nvtx = dynamo_nvtx_range!("preprocess.template");
            off_runtime(|| self.apply_template(request))
                .with_context(|| "Failed to apply prompt template")?
        };
        TEMPLATE_SECONDS.observe(template_start.elapsed().as_secs_f64());

        // Generic reasoning parsers start from `<think>`; MiniMax M3 starts
        // from `<mm:think>`. If the chat template injected that opener at the
        // end of the prompt, the model completion starts mid-reasoning.
        let prompt_injected_reasoning = Self::prompt_reasoning_prefill_for_parsers(
            self.tool_call_parser.as_deref(),
            self.runtime_config.reasoning_parser.as_deref(),
            formatted_prompt.as_ref().map(RenderedPrompt::as_str),
        );

        let tokenize_start = Instant::now();
        let (token_ids, annotations) = {
            let _nvtx = dynamo_nvtx_range!("preprocess.tokenize");
            self.gather_tokens(request, formatted_prompt.as_ref(), tracker)
                .await
                .with_context(|| "Failed to gather tokens")?
        };
        TOKENIZE_SECONDS.observe(tokenize_start.elapsed().as_secs_f64());

        let (_mm_routing_entries, image_tokens) = self
            .gather_multi_modal_data_with_image_tokens(
                request,
                &mut builder,
                formatted_prompt.as_ref().map(RenderedPrompt::as_str),
                &token_ids,
            )
            .await
            .with_context(|| "Failed to gather multimodal data")?;

        // Install tokens on the builder. Done after MM routing built its
        // view so the routing-side borrow stays cheap and builder ownership
        // moves once.
        builder.token_ids(token_ids);

        STAGE_DURATION_SECONDS
            .with_label_values(&[STAGE_PREPROCESS])
            .observe(preprocess_start.elapsed().as_secs_f64());

        if let Some(nvext) = request.nvext()
            && let Some(router_params) = &nvext.router
        {
            builder.router(Some(router_params.clone()));
        }

        let mut preprocessed = builder.build()?;
        if let Some(reasoning_ended) = Self::prompt_injected_reasoning_ended_arg(
            self.runtime_config.reasoning_parser.as_deref(),
            formatted_prompt.as_ref().map(RenderedPrompt::as_str),
        ) {
            let extra_args = preprocessed
                .extra_args
                .get_or_insert_with(|| serde_json::json!({}));
            let extra_args = extra_args
                .as_object_mut()
                .context("preprocessed extra_args must be an object")?;
            extra_args.insert("reasoning_ended".to_string(), reasoning_ended.into());
        }

        // If omitted, allow generation up to the remaining context length. Responses requests
        // preserve omission so backend adapters can compute the dynamic cap from their
        // effective prompt length/tokenization.
        //
        // Multimodal `token_ids` carry unexpanded image placeholders, so prefer
        // the MM-expanded length when available, else defer to the backend.
        let exact_prompt_len =
            Self::validate_preprocessed_token_budget(&preprocessed, self.token_budget.as_ref())?;
        if preprocessed.stop_conditions.max_tokens.is_none()
            && let Some(prompt_len) = exact_prompt_len
            // Preserve omission when the prompt itself is backend-owned. In
            // particular, setting a saturating zero here would interfere with
            // SGLang's auto-truncation before the backend sees the request.
            && !self.token_budget.is_some_and(|token_budget| {
                prompt_len >= token_budget.combined_limit as usize
                    && !token_budget.reject_prompt_overflow
            })
            && let Some(max_tokens) = Self::omitted_max_tokens_default(
                prompt_len,
                self.token_budget
                    .map(|token_budget| token_budget.combined_limit),
                options,
            )
        {
            preprocessed.stop_conditions.max_tokens = Some(max_tokens);
            // A strict limit may be smaller than the raw context window because
            // the engine reserves tokens. Revalidate the derived budget so an
            // already-over-limit prompt fails before streaming begins.
            Self::validate_preprocessed_token_budget(&preprocessed, self.token_budget.as_ref())?;
        }

        Ok((
            preprocessed,
            annotations,
            prompt_injected_reasoning,
            image_tokens,
        ))
    }

    pub fn builder<
        R: OAIChatLikeRequest
            + MediaRequestExt
            + AnnotationsProvider
            + SamplingOptionsProvider
            + StopConditionsProvider
            + OutputOptionsProvider
            + NvExtProvider,
    >(
        &self,
        request: &R,
    ) -> Result<PreprocessedRequestBuilder> {
        self.builder_with_lora(request, None)
    }

    fn builder_with_lora<
        R: OAIChatLikeRequest
            + MediaRequestExt
            + AnnotationsProvider
            + SamplingOptionsProvider
            + StopConditionsProvider
            + OutputOptionsProvider
            + NvExtProvider,
    >(
        &self,
        request: &R,
        lora_name_override: Option<String>,
    ) -> Result<PreprocessedRequestBuilder> {
        let mut builder = PreprocessedRequest::builder();
        builder.model(request.model());

        let mut stop_conditions = request.extract_stop_conditions()?;
        let eos_token_ids = self.model_info.eos_token_ids();
        let hidden_eos_token_ids = eos_token_ids.clone();
        if let Some(stop_tokens) = &mut stop_conditions.stop_token_ids_hidden {
            for eos_token_id in hidden_eos_token_ids {
                if !stop_tokens.contains(&eos_token_id) {
                    stop_tokens.push(eos_token_id);
                }
            }
        } else {
            stop_conditions.stop_token_ids_hidden = Some(hidden_eos_token_ids);
        }
        // Some tool-call parsers terminate on a token that is also a model
        // EOS (e.g. Harmony's `<|call|>` for gpt-oss). Left in the hidden
        // set, the engine stops AND strips it, so the parser sees a
        // truncated envelope and drops the call. Move such tokens to the
        // visible set so the engine still stops on them but the token
        // survives into output for the parser to consume. See PR #9778.
        let mut visible_tool_parser_end_token_ids = Vec::new();
        if let Some(stop_tokens) = &mut stop_conditions.stop_token_ids_hidden {
            visible_tool_parser_end_token_ids =
                self.remove_tool_parser_end_tokens_from_hidden_stops(request, stop_tokens)?;
        }
        if !visible_tool_parser_end_token_ids.is_empty() {
            let visible_stops = stop_conditions
                .stop_token_ids_visible
                .get_or_insert_with(Vec::new);
            for token_id in visible_tool_parser_end_token_ids {
                if !visible_stops.contains(&token_id) {
                    visible_stops.push(token_id);
                }
            }
        }

        // apply ignore eos if not already set
        stop_conditions.apply_ignore_eos();

        if !stop_conditions.ignore_eos.unwrap_or(false) {
            builder.eos_token_ids(eos_token_ids);
        }

        let has_thinking_budget = stop_conditions.max_thinking_tokens.is_some();
        if has_thinking_budget
            && self
                .runtime_config
                .supports_runtime_capability(SGLANG_GENERATE_CAPABILITY)
            && Self::has_structured_response_format(request)
            && !Self::structured_response_supports_sglang_reasoning_gate(
                self.runtime_config.reasoning_parser.as_deref(),
            )
        {
            return Err(invalid_argument_error(
                "thinking_token_budget is not supported with GPT-OSS structured output on SGLang",
            ));
        }
        builder.stop_conditions(stop_conditions);
        builder.sampling_options(request.extract_sampling_options()?);

        // Some parsers rely on `<|tool_call>`, `<|channel>`, etc. being
        // visible in the decoded text. The default `skip_special_tokens=true`
        // strips them and silently bypasses parsing. Mirror upstream's
        // per-parser `adjust_request` hook by flipping the default to false
        // for parsers that need special tokens preserved, unless the caller
        // has explicitly set `skip_special_tokens`.
        let mut output_options = request.extract_output_options()?;
        if output_options.skip_special_tokens.is_none() && self.parser_requires_special_tokens {
            output_options.skip_special_tokens = Some(false);
        } else if Self::special_tokens_will_be_stripped(
            output_options.skip_special_tokens,
            self.parser_requires_special_tokens,
        ) {
            // Caller forced `skip_special_tokens=true` while a special-token-
            // dependent parser is active. The engine's markers (e.g. harmony
            // `<|channel|>` / `<|message|>`) get stripped before the parser
            // runs, so tool_calls / reasoning_content come back empty and the
            // markup leaks into `content`. Warn once: this is a silent,
            // deterministic correctness loss that no parser fixture can catch.
            static WARNED: std::sync::Once = std::sync::Once::new();
            WARNED.call_once(|| {
                tracing::warn!(
                    tool_call_parser = ?self.tool_call_parser,
                    reasoning_parser = ?self.runtime_config.reasoning_parser,
                    "skip_special_tokens=true requested while a special-token-dependent parser is active; the engine's special tokens will be stripped before parsing, so tool_calls/reasoning_content will be empty and the markup will leak into content. Unset skip_special_tokens (Dynamo defaults it to false for these parsers) or set it to false."
                );
            });
        }
        builder.output_options(output_options);
        builder.annotations(request.annotations().unwrap_or_default());
        builder.mdc_sum(Some(self.mdcsum.clone()));
        let lora_name = self.lora_name.clone().or(lora_name_override);
        let cache_namespace = request_cache_salt(request).map(str::to_owned);

        // Extract routing hints from nvext if present
        if let Some(nvext) = request.nvext() {
            // Build routing hints from nvext fields
            let hints = nvext.agent_hints.as_ref();
            let (priority_jump, strict_priority, priority) = routing_priorities(hints);
            builder.request_timestamp_ms(nvext.request_timestamp_ms);
            let routing = RoutingHints {
                backend_instance_id: nvext.backend_instance_id,
                prefill_worker_id: nvext.prefill_worker_id,
                decode_worker_id: nvext.decode_worker_id,
                dp_rank: nvext.dp_rank,
                prefill_dp_rank: nvext.prefill_dp_rank,
                expected_output_tokens: hints.and_then(|h| h.osl),
                priority_jump,
                strict_priority,
                priority,
                lora_name,
                cache_namespace: cache_namespace.clone(),
                allowed_worker_ids: None,
                routing_constraints: nvext
                    .routing_constraints
                    .clone()
                    .map(routing_constraints_to_kv),
            };
            builder.routing(Some(routing));
        } else if lora_name.is_some() || cache_namespace.is_some() {
            // Ensure routing hints exist when we have LoRA or a legacy
            // top-level cache_salt, even when nvext is absent.
            builder.routing(Some(RoutingHints {
                lora_name,
                cache_namespace,
                ..Default::default()
            }));
        }

        if let Some(extra_args) = Self::backend_extra_args(
            request,
            self.runtime_config.reasoning_parser.is_some(),
            None,
        ) {
            builder.extra_args(Some(extra_args));
        }

        // SGLang needs this signal for guided output and per-request budgets.
        builder.require_reasoning(Self::request_requires_reasoning(
            request,
            self.runtime_config.reasoning_parser.as_deref(),
            has_thinking_budget,
        ));

        // Forward mm_processor_kwargs (e.g. use_audio_in_video) to the backend.
        builder.mm_processor_kwargs(request.mm_processor_kwargs().cloned());

        // Forward media_io_kwargs untouched only when the worker owns decoding. With a
        // media loader the frontend consumes them itself, so re-sending would risk the
        // worker applying them a second time.
        if self.media_loader.is_none() {
            builder.media_io_kwargs(request.media_io_kwargs().cloned());
        }

        Ok(builder)
    }

    fn remove_tool_parser_end_tokens_from_hidden_stops<R: OAIChatLikeRequest>(
        &self,
        request: &R,
        hidden_stop_token_ids: &mut Vec<TokenIdType>,
    ) -> Result<Vec<TokenIdType>> {
        let has_tools = Self::request_has_effective_tools(request);
        let tool_choice_none = request
            .tool_choice()
            .as_ref()
            .and_then(|tool_choice| tool_choice.as_str())
            == Some("none");

        if !Self::should_keep_tool_parser_end_tokens_visible(has_tools, tool_choice_none) {
            return Ok(Vec::new());
        }

        let Some(tool_call_parser) = self.tool_call_parser.as_deref().filter(|p| !p.is_empty())
        else {
            return Ok(Vec::new());
        };
        let Some(tool_call_config) = get_tool_parser_map().get(tool_call_parser) else {
            return Ok(Vec::new());
        };

        let mut visible_stop_token_ids = Vec::new();
        for end_token in tool_call_config.parser_config.tool_call_end_tokens() {
            if end_token.is_empty() {
                continue;
            }
            let encoded = self.tokenizer.encode(&end_token).with_context(|| {
                format!(
                    "Failed to encode tool-call end token {end_token:?} for parser {tool_call_parser:?}"
                )
            })?;
            let was_hidden_eos =
                Self::remove_single_token_marker(hidden_stop_token_ids, encoded.token_ids());
            if !was_hidden_eos {
                tracing::debug!(
                    token_ids = ?encoded.token_ids(),
                    end_token,
                    parser = tool_call_parser,
                    "Tool-call end token was not a single hidden EOS token"
                );
                continue;
            }
            if let [token_id] = encoded.token_ids()
                && !visible_stop_token_ids.contains(token_id)
            {
                visible_stop_token_ids.push(*token_id);
            }
        }

        Ok(visible_stop_token_ids)
    }

    fn request_has_effective_tools<R: OAIChatLikeRequest>(request: &R) -> bool {
        // `OAIChatLikeRequest` has no equivalent method, so this generic path mirrors
        // `CreateChatCompletionRequest::has_effective_tools`;
        // `effective_tool_predicates_match_protocol_definition` pins parity.
        request
            .tools()
            .as_ref()
            .and_then(|tools| tools.len())
            .is_some_and(|len| len > 0)
            || request.typed_messages().is_some_and(|messages| {
                messages.iter().any(|message| {
                    matches!(
                        message,
                        dynamo_protocols::types::ChatCompletionRequestMessage::System(system)
                            if system.tools.as_ref().is_some_and(|tools| !tools.is_empty())
                    )
                })
            })
    }

    fn should_keep_tool_parser_end_tokens_visible(has_tools: bool, tool_choice_none: bool) -> bool {
        has_tools && !tool_choice_none
    }

    fn remove_single_token_marker(
        hidden_eos_token_ids: &mut Vec<TokenIdType>,
        marker_token_ids: &[TokenIdType],
    ) -> bool {
        let [marker_token_id] = marker_token_ids else {
            return false;
        };
        let before = hidden_eos_token_ids.len();
        hidden_eos_token_ids.retain(|token_id| token_id != marker_token_id);
        hidden_eos_token_ids.len() != before
    }

    /// Rendering is driven by the request, so its failures are reported as 400 rather than
    /// 500, matching vLLM. A misconfigured template can also fail here, for instance a
    /// `chat_template` map that omits the `tool_use` key, so log the cause chain before it
    /// is flattened into the client-facing message.
    fn map_prompt_render_error(error: anyhow::Error) -> anyhow::Error {
        tracing::debug!(?error, "Chat template rendering failed");
        let message = match error.downcast_ref::<PromptRenderError>() {
            Some(PromptRenderError::InvalidRequest(message)) => message.clone(),
            None => format!("{error:#}"),
        };
        invalid_argument_error(message)
    }

    pub fn apply_template<
        R: OAIChatLikeRequest
            + AnnotationsProvider
            + SamplingOptionsProvider
            + StopConditionsProvider
            + OutputOptionsProvider
            + NvExtProvider
            + CommonExtProvider,
    >(
        &self,
        request: &R,
    ) -> Result<Option<RenderedPrompt>> {
        let continue_final = request.get_continue_final_message() == Some(true);
        if continue_final
            && self
                .formatter
                .media_message_order(request)
                .is_some_and(|order| {
                    order
                        .last()
                        .is_some_and(|&source| source != order.len() - 1)
                })
        {
            return Err(invalid_argument_error(
                "Cannot continue the final message because the prompt formatter moves it before other messages",
            ));
        }
        let formatted_prompt = if self.normalize_tool_call_args || continue_final {
            self.apply_template_inner(&NormalizedArgsRequest {
                inner: request,
                normalize_tool_call_args: self.normalize_tool_call_args,
                continue_final_message: continue_final,
            })?
        } else {
            self.apply_template_inner(request)?
        };
        let Some(prompt) = formatted_prompt else {
            return Ok(None);
        };
        if !continue_final {
            return Ok(Some(prompt));
        }
        apply_continue_final_message(prompt)
            .map_err(|error| invalid_argument_error(format!("{error:#}")))
            .map(Some)
    }

    fn apply_template_inner<
        R: OAIChatLikeRequest
            + AnnotationsProvider
            + SamplingOptionsProvider
            + StopConditionsProvider
            + OutputOptionsProvider
            + NvExtProvider,
    >(
        &self,
        request: &R,
    ) -> Result<Option<RenderedPrompt>> {
        if let PromptInput::Text(_) = request.prompt_input_type()
            && let Some(TextInput::Single(_)) = request.extract_text()
        {
            let use_raw_prompt = request
                .nvext()
                .is_some_and(|ext| ext.use_raw_prompt.unwrap_or(false));

            let formatted_prompt = if use_raw_prompt {
                match request.raw_prompt() {
                    Some(prompt) => RenderedPrompt::text(prompt),
                    None => {
                        tracing::warn!("Raw prompt requested but not available");
                        self.formatter
                            .render_prompt(request)
                            .map_err(Self::map_prompt_render_error)?
                    }
                }
            } else {
                self.formatter
                    .render_prompt(request)
                    .map_err(Self::map_prompt_render_error)?
            };
            Ok(Some(formatted_prompt))
        } else {
            Ok(None)
        }
    }

    /// Replace inline `data:` URLs with empty strings in message content parts.
    /// Preserves HTTP(S) URLs, text content, and overall message structure.
    fn strip_inline_data_urls(messages: &mut serde_json::Value) {
        let Some(arr) = messages.as_array_mut() else {
            return;
        };
        for msg in arr {
            let Some(content) = msg.get_mut("content") else {
                continue;
            };
            let Some(parts) = content.as_array_mut() else {
                continue;
            };
            for part in parts {
                for key in ["image_url", "video_url", "audio_url"] {
                    if let Some(media) = part.get_mut(key)
                        && let Some(url) = media.get_mut("url")
                        && url.as_str().is_some_and(|s| s.starts_with("data:"))
                    {
                        *url = serde_json::Value::String(String::new());
                    }
                }
            }
        }
    }

    fn replace_reserved_media_slot(
        media_map: &mut MultimodalDataMap,
        modality: &str,
        slot_idx: usize,
        value: MultimodalData,
    ) -> Result<()> {
        let slot = media_map
            .get_mut(modality)
            .and_then(|slots| slots.get_mut(slot_idx))
            .with_context(|| {
                format!(
                    "missing reserved multimodal slot {modality}[{slot_idx}] during media decode"
                )
            })?;
        *slot = value;
        Ok(())
    }

    pub async fn gather_multi_modal_data<
        R: OAIChatLikeRequest + MediaRequestExt + NvExtProvider,
    >(
        &self,
        request: &R,
        builder: &mut PreprocessedRequestBuilder,
        formatted_prompt: Option<&str>,
        // Worker-bound token ids; used (mm-routing only) to build the exact
        // routing sequence and atomically gate worker `mm_hashes`.
        token_ids: &[crate::protocols::TokenIdType],
    ) -> Result<Vec<MmImageEntry>> {
        let (entries, _image_tokens) = self
            .gather_multi_modal_data_with_image_tokens(
                request,
                builder,
                formatted_prompt,
                token_ids,
            )
            .await?;
        Ok(entries
            .into_iter()
            .filter_map(|entry| match entry {
                MmRoutingEntry::Image {
                    mm_hash,
                    width,
                    height,
                } => Some(MmImageEntry {
                    mm_hash,
                    width,
                    height,
                }),
                MmRoutingEntry::Video { .. } => None,
            })
            .collect())
    }

    async fn gather_multi_modal_data_with_image_tokens<
        R: OAIChatLikeRequest + MediaRequestExt + NvExtProvider,
    >(
        &self,
        request: &R,
        builder: &mut PreprocessedRequestBuilder,
        formatted_prompt: Option<&str>,
        token_ids: &[crate::protocols::TokenIdType],
    ) -> Result<(Vec<MmRoutingEntry>, Option<usize>)> {
        // `token_ids` is only consumed by exact MM-routing construction below.
        #[cfg(not(feature = "mm-routing"))]
        let _ = token_ids;

        let mut media_map: MultimodalDataMap = HashMap::new();
        let mut uuid_map: MultimodalUuidMap = HashMap::new();
        let mut has_user_uuid = false;
        // Decoded results are written back into these reserved modality slots so
        // URL-backed and UUID-only inputs retain request order.
        let mut fetch_tasks: Vec<MediaFetchTask<'_>> = Vec::new();
        #[cfg(feature = "mm-routing")]
        let mut mm_routing_entries: Vec<MmRoutingEntry> = Vec::new();
        // Private per-request total for frontend metrics. `None` means the SMG
        // counter is unavailable or checked addition overflowed.
        #[cfg(feature = "mm-routing")]
        let mut image_tokens = self.image_token_counter.as_ref().and_then(|counter| {
            // Request-budgeted processors need the rendered text and complete
            // image batch before they can report an exact metric.
            (!counter.uses_request_context_budget()).then_some(0usize)
        });
        // A raw/passthrough video, unsupported decoded-video processor, or
        // ambiguous consecutive-video layout makes exact routing unavailable.
        // In that case the whole request falls back rather than publishing a
        // partial routing view.
        #[cfg(feature = "mm-routing")]
        let mut exact_mm_routing_eligible = true;
        #[cfg(feature = "mm-routing")]
        let mut previous_routing_entry_was_video = false;
        // Total `image_url` content parts in the request. Bumped at every
        // image part regardless of which fetch path handles it. Used at
        // hash forwarding time: if fewer image entries were resolved, we omit
        // exact routing hashes for the whole request rather than
        // ship a partial / misaligned UUID list to vLLM.
        //
        // The mismatch is only reachable on the URL-passthrough path when
        // there is no media loader. Each recoverable `fetch_image_dims_uncached`
        // failure logs a warning and skips its `mm_image_entries.push`.
        // Security-policy failures remain terminal. The decoded path
        // (`has_media_loader`) propagates any fetch failure via `?`.
        #[cfg(feature = "mm-routing")]
        let mut total_image_count: usize = 0;
        // For the URL-passthrough case (media_loader is None) we collect image
        // URLs here and resolve dims via header-only HTTP after the loop so we
        // can issue all fetches in parallel.
        #[cfg(feature = "mm-routing")]
        let mut url_passthrough_images: Vec<(u64, String)> = Vec::new();

        let Some(messages) = request.typed_messages() else {
            return Ok((Vec::new(), None));
        };
        let has_media_loader = self.media_loader.is_some();

        let message_order = self.formatter.media_message_order(request);
        for index in 0..messages.len() {
            let message = &messages[message_order.as_ref().map_or(index, |order| order[index])];
            let Some(content_parts) = multimodal_content_parts(message) else {
                continue;
            };
            for content_part in content_parts {
                let Some((type_str, url, uuid)) = content_part.media_info() else {
                    continue;
                };

                #[cfg(feature = "mm-routing")]
                if type_str == "image_url" {
                    total_image_count += 1;
                }
                #[cfg(feature = "mm-routing")]
                if !exact_mm_routing_supports_modality(type_str, has_media_loader)
                    || !exact_mm_routing_layout_accepts_next_entry(
                        &mut previous_routing_entry_was_video,
                        type_str == "video_url",
                    )
                {
                    exact_mm_routing_eligible = false;
                }

                if uuid.as_deref().is_some_and(str::is_empty) {
                    return Err(invalid_argument_error(format!(
                        "{type_str} uuid must be a non-empty string"
                    )));
                }

                let slots = media_map.entry(type_str.to_string()).or_default();
                let slot_idx = slots.len();
                has_user_uuid |= uuid.is_some();
                uuid_map
                    .entry(type_str.to_string())
                    .or_default()
                    .push(uuid.clone());

                match (url, uuid) {
                    (Some(url), _) => {
                        // Every media URL passes here, with or without frontend
                        // decoding, so this applies the workers' data: URL cap.
                        if url.scheme() == "data" {
                            let size = url.as_str().len();
                            let limit = max_data_url_bytes();
                            if size > limit {
                                let message = format!(
                                    "{type_str} data: URL is {size} bytes, exceeds the {limit}-byte limit. \
                                     To raise the limit, set DYN_MM_MAX_DATA_URL_MB (in megabytes) on \
                                     both the frontend and the workers."
                                );
                                // The text holds a fixed modality key and two
                                // numbers, so the 400 body can carry it.
                                return Err(crate::protocols::common::invalid_argument_error(
                                    message,
                                ));
                            }
                        }
                        if has_media_loader {
                            fetch_tasks.push(MediaFetchTask {
                                modality: type_str,
                                slot_idx,
                                content_part: content_part.as_user(),
                            });
                        } else {
                            #[cfg(feature = "mm-routing")]
                            if type_str == "image_url" {
                                let mm_hash = Self::hash_image_url(url.as_str());
                                url_passthrough_images.push((mm_hash, url.as_str().to_string()));
                            }
                        }
                        slots.push(MultimodalData::Url(url));
                    }
                    (None, Some(uuid)) if type_str == "image_url" => {
                        slots.push(MultimodalData::UuidOnly(uuid));
                    }
                    (None, Some(_)) => {
                        return Err(invalid_argument_error(format!(
                            "UUID-only cache reuse is not supported for media modality `{type_str}`; provide a media URL"
                        )));
                    }
                    (None, None) => {
                        return Err(invalid_argument_error(format!(
                            "{type_str} part has neither `url` nor `uuid`; at least one is required"
                        )));
                    }
                }
            }
        }

        #[cfg(feature = "mm-routing")]
        let has_processor_override = has_mm_processor_override(request.mm_processor_kwargs());
        #[cfg(all(feature = "mm-routing", feature = "media-ffmpeg"))]
        let hash_decoded_video = should_hash_decoded_video(
            exact_mm_routing_eligible,
            has_user_uuid,
            has_processor_override,
            self.video_routing_processor.is_some(),
        );
        #[cfg(not(all(feature = "mm-routing", feature = "media-ffmpeg")))]
        let hash_decoded_video = false;

        // Execute all fetch tasks
        if !fetch_tasks.is_empty() {
            let loader = self.media_loader.as_ref().unwrap();
            // The frontend owns decoding here, so the opaque request kwargs are parsed
            // into the decoder config -- borrowing the `Value`, no clone. Mapped to
            // InvalidArgument so a malformed payload stays a 400 rather than a 500.
            let media_io_kwargs = request
                .media_io_kwargs()
                .map(<MediaDecoder as serde::Deserialize>::deserialize)
                .transpose()
                .map_err(|e| invalid_argument_error(format!("invalid media_io_kwargs: {e}")))?;
            let results = futures::future::join_all(fetch_tasks.iter().map(|task| {
                loader.fetch_and_decode_media_part_with_video_hash(
                    task.content_part.as_ref(),
                    media_io_kwargs.as_ref(),
                    hash_decoded_video,
                )
            }))
            .await;

            for (task, result) in fetch_tasks.into_iter().zip(results) {
                // if one item fails, errors the whole request, other items will be cleaned up by Drop
                let rdma_descriptor = result?;

                #[cfg(feature = "mm-routing")]
                if task.modality == "image_url" {
                    let shape = &rdma_descriptor.tensor_info.shape;
                    if shape.len() >= 2 {
                        let h = shape[0] as u32;
                        let w = shape[1] as u32;
                        // Frontend-decode path: hash the decoded RGB bytes so
                        // the same image reached via different (signed) URLs
                        // collides on the same `mm_hash` and routes to the
                        // worker that already has those KV blocks. Fall back
                        // to URL hashing only if the descriptor lost local
                        // storage (e.g. reconstructed from the wire), which
                        // shouldn't happen on the frontend.
                        let (mm_hash, hash_source) = match rdma_descriptor.content_hash() {
                            Some(h) => (h, "decoded_bytes"),
                            None => {
                                let source_url = image_content_part_url(task.content_part.as_ref())
                                    .ok_or_else(|| {
                                        invalid_argument_error(
                                            "image_url task must contain an image URL",
                                        )
                                    })?;
                                (Self::hash_image_url(source_url), "url_fallback")
                            }
                        };
                        if let Some(counter) = self
                            .image_token_counter
                            .as_ref()
                            .filter(|counter| !counter.uses_request_context_budget())
                        {
                            let n = counter.count_tokens(w, h);
                            tracing::debug!(
                                target: "mm_routing",
                                model = counter.model_id(),
                                width = w,
                                height = h,
                                tokens = n,
                                mm_hash = mm_hash,
                                source = hash_source,
                                "image-token count"
                            );
                            image_tokens = checked_add_image_tokens(image_tokens, n);
                        }
                        mm_routing_entries.push(MmRoutingEntry::Image {
                            mm_hash,
                            width: w,
                            height: h,
                        });
                    }
                } else if task.modality == "video_url" {
                    #[cfg(feature = "media-ffmpeg")]
                    {
                        let video_entry = (|| -> Result<MmRoutingEntry> {
                            let processor =
                                self.video_routing_processor.as_ref().ok_or_else(|| {
                                    anyhow::anyhow!("model has no exact video routing processor")
                                })?;
                            anyhow::ensure!(
                                !has_processor_override,
                                "request mm_processor_kwargs can change the video token layout"
                            );
                            let (frame_count, width, height) =
                                rdma_descriptor.video_dimensions()?;
                            let metadata = rdma_descriptor.video_metadata()?;
                            let mm_hash = rdma_descriptor.video_content_hash()?;
                            let routing =
                                processor.build_replacement(&mm_routing::VideoRoutingInput {
                                    frame_count,
                                    width,
                                    height,
                                    source_fps: metadata.source_fps,
                                    sampled_timestamps: &metadata.sampled_timestamps,
                                })?;
                            tracing::debug!(
                                target: "mm_routing",
                                n_frames = frame_count,
                                width,
                                height,
                                replacement_tokens = routing.replacement_tokens.len(),
                                mm_hash,
                                "video routing metadata resolved"
                            );
                            Ok(MmRoutingEntry::Video {
                                mm_hash,
                                placeholder_token_id: routing.placeholder_token_id,
                                event_video_token_id: routing.event_video_token_id,
                                target_tokens: routing.target_tokens,
                                replacement_tokens: routing.replacement_tokens,
                            })
                        })();
                        match video_entry {
                            Ok(entry) => mm_routing_entries.push(entry),
                            Err(error) => {
                                exact_mm_routing_eligible = false;
                                tracing::debug!(
                                    target: "mm_routing",
                                    %error,
                                    "mm-routing: decoded video is not eligible for exact routing; falling back to text-prefix routing"
                                );
                            }
                        }
                    }
                    #[cfg(not(feature = "media-ffmpeg"))]
                    {
                        exact_mm_routing_eligible = false;
                    }
                }

                Self::replace_reserved_media_slot(
                    &mut media_map,
                    task.modality,
                    task.slot_idx,
                    MultimodalData::Decoded(rdma_descriptor),
                )?;
            }
        }

        // URL-passthrough path (media_loader is None): fetch image headers in
        // parallel to get (W, H) per image without downloading the full bytes.
        // Enables MM-aware routing for backends that register
        // `media_decoder: null` and decode images on the worker.
        #[cfg(feature = "mm-routing")]
        if !has_user_uuid && !url_passthrough_images.is_empty() {
            let dim_results =
                futures::future::join_all(url_passthrough_images.iter().map(|(mm_hash, url)| {
                    Self::fetch_image_dims(*mm_hash, url, self.routing_image_dimension_policy)
                }))
                .await;
            for ((mm_hash, url), dim_res) in url_passthrough_images.into_iter().zip(dim_results) {
                match dim_res {
                    Ok((w, h)) => {
                        if let Some(counter) = self
                            .image_token_counter
                            .as_ref()
                            .filter(|counter| !counter.uses_request_context_budget())
                        {
                            let n = counter.count_tokens(w, h);
                            tracing::debug!(
                                target: "mm_routing",
                                model = counter.model_id(),
                                width = w,
                                height = h,
                                tokens = n,
                                mm_hash = mm_hash,
                                source = "url_passthrough_header_fetch",
                                "image-token count"
                            );
                            image_tokens = checked_add_image_tokens(image_tokens, n);
                        }
                        mm_routing_entries.push(MmRoutingEntry::Image {
                            mm_hash,
                            width: w,
                            height: h,
                        });
                    }
                    Err(e) => {
                        if MediaFetcher::is_policy_rejection(&e) {
                            return Err(e);
                        }
                        // Redact `data:` URIs to just the media-type prefix —
                        // the comma-separated payload is the entire (base64)
                        // image body and ships in logs would be log bloat /
                        // potential PII spillage if logs are aggregated.
                        let url_for_log = if url.starts_with("data:") {
                            url.split_once(',')
                                .map(|(p, _)| format!("{p},<redacted>"))
                                .unwrap_or_else(|| "data:<redacted>".to_string())
                        } else {
                            url.to_string()
                        };
                        tracing::warn!(
                            target: "mm_routing",
                            url = %url_for_log,
                            error = %e,
                            "mm-routing: failed to fetch image dims; MM routing entry skipped"
                        );
                    }
                }
            }
        }

        if !media_map.is_empty() {
            builder.multi_modal_data(Some(media_map));
            if has_user_uuid {
                builder.multi_modal_uuids(Some(uuid_map));
            }

            // User cache identities are opaque and cannot be converted into the
            // router's canonical image hashes. Fall back to text-prefix routing
            // instead of routing and publishing under different cache keys.
            #[cfg(feature = "mm-routing")]
            if has_user_uuid {
                mm_routing_entries.clear();
            }

            // Preserve original messages and formatted prompt in extra_args for multimodal
            // workers (e.g., TRT-LLM needs message structure and the template-rendered
            // prompt with <image> placeholders for embedding-path / NIXL flows).
            let messages_json = serde_json::to_value(request.messages())?;
            let mut extra_args = serde_json::json!({
                "messages": messages_json
            });

            // `multi_modal_data` already carries the media (decoded descriptors or
            // original URLs, including inline `data:`). Duplicating those payloads
            // in `extra_args.messages` doubles the request-plane frame and can
            // exceed DYN_TCP_MAX_MESSAGE_SIZE. Strip inline data; keep HTTP(S)
            // URLs and the chat-template message structure.
            Self::strip_inline_data_urls(&mut extra_args["messages"]);

            if let Some(prompt) = formatted_prompt {
                // Clone here is the single owned allocation we actually need:
                // the prompt is inserted into the request's `extra_args` JSON.
                // The caller still holds the original `String`; passing
                // `Option<&str>` keeps text-only requests (no MM) clone-free.
                extra_args["formatted_prompt"] = serde_json::Value::String(prompt.to_string());
            }

            if let Some(serde_json::Value::Object(backend_extra_args)) = Self::backend_extra_args(
                request,
                self.runtime_config.reasoning_parser.is_some(),
                Self::prompt_injected_reasoning_ended_arg(
                    self.runtime_config.reasoning_parser.as_deref(),
                    formatted_prompt,
                ),
            ) {
                let extra_args_obj = extra_args
                    .as_object_mut()
                    .expect("multimodal extra_args must be an object");
                extra_args_obj.extend(backend_extra_args);
            }

            // Build and install routing info + worker hashes atomically. If any
            // mixed-media precondition misses, neither side receives exact keys
            // and the request cleanly uses text-prefix routing.
            #[cfg(feature = "mm-routing")]
            let resolved_image_count = mm_routing_entries
                .iter()
                .filter(|entry| matches!(entry, MmRoutingEntry::Image { .. }))
                .count();
            #[cfg(feature = "mm-routing")]
            let exact_mm_routing_ready = exact_mm_routing_eligible
                && exact_mm_routing_preconditions_met(
                    has_user_uuid,
                    resolved_image_count,
                    total_image_count,
                    has_processor_override,
                );
            #[cfg(feature = "mm-routing")]
            let request_budgeted_image_counter = self
                .image_token_counter
                .as_ref()
                .filter(|counter| counter.uses_request_context_budget());
            #[cfg(feature = "mm-routing")]
            let image_text_prompt_len = if exact_mm_routing_ready {
                match request_budgeted_image_counter {
                    Some(counter) => {
                        counter
                            .context_budget_text_len(
                                self.tokenizer.clone(),
                                formatted_prompt,
                                resolved_image_count,
                            )
                            .await
                    }
                    None => None,
                }
            } else {
                None
            };
            #[cfg(feature = "mm-routing")]
            if let Some(counter) = request_budgeted_image_counter {
                image_tokens = image_text_prompt_len.and_then(|text_prompt_len| {
                    let dimensions: Vec<_> = mm_routing_entries
                        .iter()
                        .filter_map(|entry| match entry {
                            MmRoutingEntry::Image { width, height, .. } => Some((*width, *height)),
                            MmRoutingEntry::Video { .. } => None,
                        })
                        .collect();
                    counter
                        .count_tokens_for_images(
                            &dimensions,
                            self.context_length as usize,
                            text_prompt_len,
                        )
                        .ok()
                        .and_then(|counts| counts.into_iter().try_fold(0usize, usize::checked_add))
                });
            }
            #[cfg(feature = "mm-routing")]
            let mm_routing_info = if exact_mm_routing_ready {
                self.build_mm_exact_routing_info(
                    &mm_routing_entries,
                    token_ids,
                    image_text_prompt_len,
                )
            } else {
                None
            };
            #[cfg(feature = "mm-routing")]
            if let Some(mm_routing_info) = mm_routing_info {
                let hex = |entry: &MmRoutingEntry| {
                    serde_json::Value::String(format!("{:016x}", entry.mm_hash()))
                };
                if mm_routing_entries
                    .iter()
                    .all(|entry| matches!(entry, MmRoutingEntry::Image { .. }))
                {
                    // Preserve the legacy image-only worker protocol.
                    extra_args["mm_hashes"] =
                        serde_json::Value::Array(mm_routing_entries.iter().map(hex).collect());
                } else {
                    let mut grouped = serde_json::Map::new();
                    let image_hashes: Vec<_> = mm_routing_entries
                        .iter()
                        .filter(|entry| matches!(entry, MmRoutingEntry::Image { .. }))
                        .map(hex)
                        .collect();
                    let video_hashes: Vec<_> = mm_routing_entries
                        .iter()
                        .filter(|entry| matches!(entry, MmRoutingEntry::Video { .. }))
                        .map(hex)
                        .collect();
                    if !image_hashes.is_empty() {
                        grouped.insert("image".to_string(), serde_json::Value::Array(image_hashes));
                    }
                    if !video_hashes.is_empty() {
                        grouped.insert("video".to_string(), serde_json::Value::Array(video_hashes));
                    }
                    extra_args["mm_hashes_by_modality"] = serde_json::Value::Object(grouped);
                }
                builder.mm_routing_info(Some(mm_routing_info));
            } else if has_processor_override && total_image_count > 0 {
                tracing::debug!(
                    target: "mm_routing",
                    "mm-routing: exact MM routing disabled because mm_processor_kwargs is non-empty"
                );
            } else if !mm_routing_entries.is_empty() {
                tracing::warn!(
                    target: "mm_routing",
                    resolved = mm_routing_entries.len(),
                    expected_images = total_image_count,
                    "mm-routing: exact MM routing info not built (media resolution or placeholder-order mismatch); skipping mm_hashes forwarding"
                );
            }

            builder.extra_args(Some(extra_args));
        }

        #[cfg(feature = "mm-routing")]
        let image_tokens = aggregate_image_tokens(
            image_tokens,
            mm_routing_entries
                .iter()
                .filter(|entry| matches!(entry, MmRoutingEntry::Image { .. }))
                .count(),
            total_image_count,
            has_processor_override,
        );
        #[cfg(feature = "mm-routing")]
        return Ok((mm_routing_entries, image_tokens));
        #[cfg(not(feature = "mm-routing"))]
        Ok((Vec::new(), None))
    }

    /// Build image-only exact routing info without changing the worker-bound
    /// token IDs. Kept as the public compatibility surface; request
    /// preprocessing uses the mixed-media builder internally.
    #[cfg(feature = "mm-routing")]
    pub fn gather_mm_exact_routing_info(
        &self,
        builder: &mut PreprocessedRequestBuilder,
        image_entries: &[MmImageEntry],
        token_ids: &[crate::protocols::TokenIdType],
    ) -> Result<()> {
        let entries: Vec<_> = image_entries
            .iter()
            .map(|entry| MmRoutingEntry::Image {
                mm_hash: entry.mm_hash,
                width: entry.width,
                height: entry.height,
            })
            .collect();
        if let Some(info) = self.build_mm_exact_routing_info(&entries, token_ids, None) {
            builder.mm_routing_info(Some(info));
        }
        Ok(())
    }

    #[cfg(feature = "mm-routing")]
    fn build_mm_exact_routing_info(
        &self,
        entries: &[MmRoutingEntry],
        token_ids: &[crate::protocols::TokenIdType],
        image_text_prompt_len: Option<usize>,
    ) -> Option<crate::protocols::common::preprocessor::MmRoutingInfo> {
        use crate::protocols::common::preprocessor::MmRoutingInfo;

        if entries.is_empty() {
            return None;
        }
        if !exact_mm_routing_entries_are_unambiguous(entries) {
            tracing::debug!(
                target: "mm_routing",
                "consecutive video objects cannot be mapped exactly in vLLM KV events; skipping MM routing info"
            );
            return None;
        }
        let image_token_id = self.routing_image_token_id;
        let image_counter_required = entries
            .iter()
            .any(|entry| matches!(entry, MmRoutingEntry::Image { .. }));
        if image_counter_required && self.image_token_counter.is_none() {
            tracing::debug!(
                target: "mm_routing",
                "image_token_counter unavailable; skipping MM routing info"
            );
            return None;
        }
        if image_counter_required && image_token_id.is_none() {
            tracing::debug!(
                target: "mm_routing",
                "routing_image_token_id unresolved; skipping MM routing info"
            );
            return None;
        }
        if image_counter_required && self.routing_image_prompt_layout.is_none() {
            tracing::debug!(
                target: "mm_routing",
                "routing_image_prompt_layout unresolved; skipping MM routing info"
            );
            return None;
        }
        let block_size = self.kv_cache_block_size;
        if block_size == 0 {
            tracing::debug!(
                target: "mm_routing",
                "kv_cache_block_size is 0; skipping MM routing info"
            );
            return None;
        }
        let (mut expanded, expanded_prompt_len, block_mm_infos) = if entries
            .iter()
            .all(|entry| matches!(entry, MmRoutingEntry::Image { .. }))
        {
            let counter = self
                .image_token_counter
                .as_ref()
                .expect("image counter requirement checked above");
            let images: Vec<MmImageEntry> = entries
                .iter()
                .map(|entry| match entry {
                    MmRoutingEntry::Image {
                        mm_hash,
                        width,
                        height,
                    } => MmImageEntry {
                        mm_hash: *mm_hash,
                        width: *width,
                        height: *height,
                    },
                    MmRoutingEntry::Video { .. } => unreachable!("all entries checked as images"),
                })
                .collect();
            let image_token_id = image_token_id.expect("image token requirement checked above");
            let placeholder_count = token_ids
                .iter()
                .filter(|&&token_id| token_id == image_token_id)
                .count();
            if placeholder_count != images.len() {
                tracing::warn!(
                    target: "mm_routing",
                    placeholder_count,
                    image_count = images.len(),
                    routing_image_token_id = image_token_id,
                    "placeholder token count in tokenized prompt does not match image count; \
                     skipping MM routing info (text-prefix routing only)"
                );
                return None;
            }
            let dimensions: Vec<(u32, u32)> = images
                .iter()
                .map(|image| (image.width, image.height))
                .collect();
            let text_prompt_len = if counter.uses_request_context_budget() {
                match image_text_prompt_len {
                    Some(text_prompt_len) => text_prompt_len,
                    None => {
                        tracing::debug!(
                            target: "mm_routing",
                            model = counter.model_id(),
                            "request text length unavailable for batch image budgeting; skipping MM routing info"
                        );
                        return None;
                    }
                }
            } else {
                token_ids.len()
            };
            let n_tokens = match counter.count_tokens_for_images(
                &dimensions,
                self.context_length as usize,
                text_prompt_len,
            ) {
                Ok(counts) => counts,
                Err(error) => {
                    tracing::warn!(
                        target: "mm_routing",
                        model = counter.model_id(),
                        %error,
                        "request-wide image-token accounting failed; skipping MM routing info"
                    );
                    return None;
                }
            };
            let (expanded, expanded_prompt_len) = try_expand_mm_routing_tokens(
                self.tokenizer.as_ref(),
                self.routing_image_prompt_layout
                    .expect("image prompt layout requirement checked above"),
                self.routing_prepend_bos,
                image_token_id,
                &images,
                &n_tokens,
                token_ids,
                counter.model_id(),
            )?;
            (expanded, expanded_prompt_len, Vec::new())
        } else {
            if image_counter_required
                && self.image_token_counter.as_ref().is_some_and(
                    mm_routing::image::ImageRoutingProcessor::uses_request_context_budget,
                )
            {
                tracing::debug!(
                    target: "mm_routing",
                    "request-budgeted image expansion is unsupported for mixed modalities; skipping MM routing info"
                );
                return None;
            }
            let mut replacements = Vec::with_capacity(entries.len());
            let video_token_id = entries.iter().find_map(|entry| match entry {
                MmRoutingEntry::Video {
                    event_video_token_id,
                    ..
                } => *event_video_token_id,
                MmRoutingEntry::Image { .. } => None,
            });
            if video_token_id.is_none() && entries.len() != 1 {
                tracing::debug!(
                    target: "mm_routing",
                    "shared image/video placeholders support one video-only object; skipping exact routing for this media layout"
                );
                return None;
            }
            for entry in entries {
                let replacement = match entry {
                    MmRoutingEntry::Image {
                        mm_hash,
                        width,
                        height,
                    } => {
                        let counter = self
                            .image_token_counter
                            .as_ref()
                            .expect("image counter requirement checked above");
                        let token_count = counter.count_tokens(*width, *height);
                        let mut routing_tokens = Vec::with_capacity(token_count);
                        let mut worker_tokens = Vec::with_capacity(token_count);
                        let image = MmImageEntry {
                            mm_hash: *mm_hash,
                            width: *width,
                            height: *height,
                        };
                        let layout = self
                            .routing_image_prompt_layout
                            .expect("image prompt layout requirement checked above");
                        let image_token_id =
                            image_token_id.expect("image token requirement checked above");
                        let result = append_mm_routing_replacement(
                            &mut routing_tokens,
                            self.tokenizer.as_ref(),
                            layout,
                            image,
                            token_count,
                        )
                        .and_then(|()| {
                            append_mm_routing_replacement_with_fill(
                                &mut worker_tokens,
                                self.tokenizer.as_ref(),
                                layout,
                                image,
                                token_count,
                                image_token_id,
                            )
                        });
                        if let Err(error) = result {
                            tracing::warn!(
                                target: "mm_routing",
                                model = counter.model_id(),
                                %error,
                                "routing-only image prompt expansion failed; skipping MM routing info"
                            );
                            return None;
                        }
                        TrackedMmRoutingReplacement {
                            mm_hash: *mm_hash,
                            target_tokens: vec![image_token_id],
                            worker_tokens,
                            routing_tokens,
                        }
                    }
                    MmRoutingEntry::Video {
                        mm_hash,
                        placeholder_token_id,
                        event_video_token_id: _,
                        target_tokens,
                        replacement_tokens,
                    } => {
                        let fill_token =
                            dynamo_kv_router::protocols::pad_value_for_mm_hash(*mm_hash);
                        TrackedMmRoutingReplacement {
                            mm_hash: *mm_hash,
                            target_tokens: target_tokens.clone(),
                            worker_tokens: replacement_tokens.clone(),
                            routing_tokens: replacement_tokens
                                .iter()
                                .map(|replacement_id| {
                                    if *replacement_id == *placeholder_token_id {
                                        fill_token
                                    } else {
                                        *replacement_id
                                    }
                                })
                                .collect(),
                        }
                    }
                };
                replacements.push(replacement);
            }

            // Configured BOS is image-specific; video-only routing starts from
            // the frontend-tokenized prompt.
            let routing_bos =
                routing_bos_to_prepend(self.routing_prepend_bos, image_counter_required);
            #[cfg(feature = "media-ffmpeg")]
            let kv_event_mm_identity = self
                .video_routing_processor
                .as_ref()
                .map_or(mm_routing::KvEventMmIdentity::MmMetadata, |processor| {
                    processor.kv_event_mm_identity()
                });
            #[cfg(not(feature = "media-ffmpeg"))]
            let kv_event_mm_identity = mm_routing::KvEventMmIdentity::MmMetadata;
            match apply_tracked_mm_replacements(
                routing_bos,
                &replacements,
                token_ids,
                block_size,
                image_token_id,
                video_token_id,
                kv_event_mm_identity,
            ) {
                Ok(expanded) => expanded,
                Err(error) => {
                    tracing::warn!(
                        target: "mm_routing",
                        media_count = entries.len(),
                        %error,
                        "placeholder token sequence does not match multimodal content order; \
                         skipping MM routing info (text-prefix routing only)"
                    );
                    return None;
                }
            }
        };

        // Pad to a whole multiple of kv_cache_block_size. The router's
        // compute_block_hash_for_seq only hashes whole blocks, so the partial
        // tail block doesn't influence routing either way; aligning the length
        // keeps our routing_token_ids and `block_mm_infos` agreeing on count.
        // `div_ceil` guarantees `total_tokens >= expanded.len()`, so resize
        // only ever grows.
        let total_tokens = expanded.len().div_ceil(block_size) * block_size;
        if expanded.len() < total_tokens {
            expanded.resize(total_tokens, 0);
        }

        // Exact blocks carry MM identity in pad-value tokens. Ambiguous
        // feature-span boundaries carry the same block metadata vLLM hashes.
        tracing::debug!(
            target: "mm_routing",
            n_media = entries.len(),
            block_size,
            total_tokens,
            "MmRoutingInfo built (exact, pad_value)"
        );

        Some(MmRoutingInfo {
            routing_token_ids: expanded,
            block_mm_infos,
            expanded_prompt_len,
        })
    }

    /// xxh3-64 of the raw URL bytes. Used as the routing `mm_hash` in the
    /// URL-passthrough path: two requests with byte-identical URLs route to
    /// the same worker, anything else routes independently.
    ///
    /// We deliberately do NOT strip cache-buster / signed-URL query
    /// parameters — a query string like `?v=2` could mean either "new
    /// cache-busted fetch of the same image" or "version 2 of a different
    /// image", and the URL alone doesn't tell us which. Keeping the hash
    /// URL-identical avoids the heuristic and the false-positive collisions
    /// that come with it. Workloads with rotating signed URLs (S3, GCS,
    /// Azure SAS) should use `--frontend-decoding`: that path hashes the
    /// decoded RGB bytes instead, so cross-URL cache reuse is restored
    /// without depending on URL conventions.
    #[cfg(feature = "mm-routing")]
    fn hash_image_url(url: &str) -> u64 {
        xxhash_rust::xxh3::xxh3_64(url.as_bytes())
    }

    /// Header-only image dim fetch. For HTTP/HTTPS we issue a Range request
    /// for the first 64 KB (covers PNG/WebP in <1 KB and JPEG SOF in worst
    /// case). For data: URIs we decode the base64 payload locally and parse
    /// the header. Non-policy failures make the MM routing entry unavailable.
    /// Policy rejections remain terminal for the request.
    ///
    /// Results are cached by `(mm_hash, dimension_policy)` so repeated
    /// requests for the same image (typical of multi-turn / session workloads)
    /// hit the cache and skip the HTTP fetch entirely. Without this cache,
    /// sticky-routing workloads pay 4–5× HTTP Range fetches per request just
    /// to compute routing tokens.
    #[cfg(feature = "mm-routing")]
    async fn fetch_image_dims(
        mm_hash: u64,
        url: &str,
        dimension_policy: RoutingImageDimensionPolicy,
    ) -> Result<(u32, u32)> {
        use moka::future::Cache;
        use std::sync::LazyLock;

        // Bounded sharded LRU (moka uses TinyLFU internally — sharded write
        // locks, lock-free reads). Replaces an earlier hand-rolled DashMap +
        // tokio::sync::Notify singleflight; moka's `try_get_with` provides
        // both singleflight and bounded LRU eviction in one primitive.
        //
        //   max_capacity:  100k entries (~5 MB at ~50 B/entry incl. moka
        //                  bookkeeping). Caps memory under unbounded URL
        //                  pools (signed-URL refresh, image proxies).
        //   time_to_live:  24h. Bounds staleness if a URL is re-uploaded
        //                  with new content. Independent of capacity-based
        //                  eviction, which kicks in earlier under load.
        static DIM_CACHE: LazyLock<Cache<(u64, RoutingImageDimensionPolicy), (u32, u32)>> =
            LazyLock::new(|| {
                Cache::builder()
                    .max_capacity(100_000)
                    .time_to_live(std::time::Duration::from_secs(24 * 60 * 60))
                    .build()
            });

        // Hot path: avoid allocating an owned URL on cache hit. moka's
        // `get` is async because it may do a small amount of bookkeeping
        // for the LRU/TinyLFU policy.
        let cache_key = (mm_hash, dimension_policy);
        if let Some(dims) = DIM_CACHE.get(&cache_key).await {
            return Ok(dims);
        }

        // Cold path: take an owned String so the init future can be
        // 'static (moka may move waiters across executor threads). Because
        // try_get_with does built-in singleflight, concurrent callers for
        // the same `mm_hash` collapse into a single fetch.
        let url_owned = url.to_string();
        DIM_CACHE
            .try_get_with(cache_key, async move {
                Self::fetch_image_dims_uncached(&url_owned, dimension_policy)
                    .await
                    .map_err(ImageDimFetchFailure::from_error)
            })
            .await
            .map_err(|error| error.to_error())
    }

    #[cfg(feature = "mm-routing")]
    async fn fetch_image_dims_uncached(
        url: &str,
        dimension_policy: RoutingImageDimensionPolicy,
    ) -> Result<(u32, u32)> {
        // Most JPEG SOF markers and PNG/WebP headers fit in the first 4 KB.
        // Start small and only escalate to 64 KB if the parser fails on the
        // truncated header.
        const SMALL_RANGE: usize = 4 * 1024 - 1;
        const LARGE_RANGE: usize = 64 * 1024 - 1;
        // Per-Range tighter bound than MediaFetcher's 30 s default — dim
        // fetch is best-effort; on a slow remote we'd rather skip MM
        // routing for this image than starve the request.
        const DIM_FETCH_TIMEOUT: std::time::Duration = std::time::Duration::from_secs(10);

        if let Some(rest) = url.strip_prefix("data:") {
            let comma = rest
                .find(',')
                .ok_or_else(|| anyhow::anyhow!("malformed data URI: no comma"))?;
            let prefix = &rest[..comma];
            let payload = &rest[comma + 1..];
            let bytes: Vec<u8> = if prefix.contains(";base64") {
                use base64::Engine;
                base64::engine::general_purpose::STANDARD
                    .decode(payload)
                    .map_err(|e| anyhow::anyhow!("data URI base64 decode: {}", e))?
            } else {
                payload.as_bytes().to_vec()
            };
            return Self::dimensions_from_image_bytes(&bytes, dimension_policy);
        }

        if !(url.starts_with("http://") || url.starts_with("https://")) {
            anyhow::bail!("unsupported url scheme for dim fetch: {}", url);
        }

        // `DIM_FETCH_MEDIA_FETCHER` and `DIM_FETCH_HTTP_CLIENT` are
        // module-scope `LazyLock`s forced at startup in `new_with_parts`
        // for MM-routable preprocessors — see their definitions for the
        // lifecycle and policy contract.

        // Pre-flight SSRF check on the original URL. Redirect targets are
        // revalidated by the Client's redirect policy, and DNS-resolved
        // IPs are filtered by the resolver — so a URL that passes here
        // can't escape the contract on the wire either.
        let parsed = url::Url::parse(url)?;
        DIM_FETCH_MEDIA_FETCHER
            .check_if_url_allowed_with_dns(&parsed)
            .await?;

        let mut range_end = SMALL_RANGE;
        loop {
            let resp = DIM_FETCH_HTTP_CLIENT
                .get(url)
                .header("Range", format!("bytes=0-{}", range_end))
                .timeout(DIM_FETCH_TIMEOUT)
                .send()
                .await
                .map_err(|error| {
                    crate::preprocessor::media::MediaFetcher::map_fetch_error(error.into())
                })?;
            let status = resp.status();
            // Require 206 Partial Content — if the origin ignored the
            // Range header and answered 200 OK, `.bytes()` would buffer
            // the full image into memory. Bail in that case rather than
            // download an unbounded payload just to peek at dimensions.
            // The caller treats Err as "MM routing entry unavailable for
            // this image", which falls back to text-prefix routing.
            if status != reqwest::StatusCode::PARTIAL_CONTENT {
                anyhow::bail!(
                    "image dim fetch expected 206 Partial Content, got HTTP {}",
                    status
                );
            }
            let bytes = resp.bytes().await?;
            match Self::dimensions_from_image_bytes(&bytes, dimension_policy) {
                Ok((w, h)) => return Ok((w, h)),
                Err(_) if range_end < LARGE_RANGE => {
                    range_end = LARGE_RANGE;
                    continue;
                }
                Err(e) => anyhow::bail!("image header parse failed after 64KB: {}", e),
            }
        }
    }

    #[cfg(feature = "mm-routing")]
    fn dimensions_from_image_bytes(
        bytes: &[u8],
        dimension_policy: RoutingImageDimensionPolicy,
    ) -> Result<(u32, u32)> {
        use image::{ImageDecoder, ImageReader, metadata::Orientation};
        use std::io::Cursor;

        let reader = ImageReader::new(Cursor::new(bytes)).with_guessed_format()?;
        if dimension_policy == RoutingImageDimensionPolicy::Encoded {
            return Ok(reader.into_dimensions()?);
        }

        let mut decoder = reader.into_decoder()?;
        let (width, height) = decoder.dimensions();
        let swaps_axes = matches!(
            decoder.orientation()?,
            Orientation::Rotate90
                | Orientation::Rotate270
                | Orientation::Rotate90FlipH
                | Orientation::Rotate270FlipH
        );
        Ok(if swaps_axes {
            (height, width)
        } else {
            (width, height)
        })
    }

    /// Tokenize the request and return the token ids alongside any annotations
    /// the caller asked for. The caller owns the result and is responsible for
    /// installing it on the builder via `builder.token_ids(...)` once any
    /// downstream consumers (e.g. MM-routing) have borrowed it.
    pub async fn gather_tokens<
        R: OAIChatLikeRequest
            + AnnotationsProvider
            + SamplingOptionsProvider
            + StopConditionsProvider
            + OutputOptionsProvider
            + NvExtProvider,
    >(
        &self,
        request: &R,
        formatted_prompt: Option<&RenderedPrompt>,
        tracker: Option<&RequestTracker>,
    ) -> Result<(Vec<crate::protocols::TokenIdType>, HashMap<String, String>)> {
        let mut annotations = HashMap::new();
        let mut tokens_out: Vec<crate::protocols::TokenIdType> = Vec::new();
        // match request type before any conversion/processing
        match request.prompt_input_type() {
            PromptInput::Tokens(_) => {
                if let Some(token_input) = request.extract_tokens() {
                    match token_input {
                        TokenInput::Single(tokens) => {
                            tokens_out = tokens;
                        }
                        TokenInput::Batch(token_batches) => {
                            if token_batches.len() == 1 {
                                tokens_out = token_batches[0].clone();
                            } else {
                                bail!(
                                    "Batch token input not supported for more than one token in requests (got {})",
                                    token_batches.len()
                                );
                            }
                        }
                    }
                    // A token prompt skips the tokenizer, so bound its ids here.
                    ensure_token_ids_in_vocab("prompt", &tokens_out, self.token_id_bound)?;
                }
            }
            PromptInput::Text(_) => {
                if let Some(text_input) = request.extract_text() {
                    match text_input {
                        TextInput::Single(raw_prompt) => {
                            if let Some(f) = formatted_prompt
                                && request.has_annotation(ANNOTATION_FORMATTED_PROMPT)
                            {
                                annotations.insert(
                                    ANNOTATION_FORMATTED_PROMPT.to_string(),
                                    f.as_str().to_string(),
                                );
                            }

                            // Completions will use raw_prompt, no template.
                            // K3 keeps control-token spans distinct from user text until
                            // tokenization; all other renderers use the plain text path.

                            // If nvext.token_data is present, use the pre-computed tokens
                            // directly and skip tokenization.  This avoids redundant
                            // tokenization when an external component (e.g. the GAIE EPP
                            // KV-router) has already tokenized the prompt.
                            // When backend_instance_id is set without token_data, warn
                            // but fall back to tokenization (backward compat for non-GAIE
                            // routers that set the header without providing tokens).
                            let has_backend_instance_id = request
                                .nvext()
                                .and_then(|ext| ext.backend_instance_id)
                                .is_some();

                            let token_data =
                                request.nvext().and_then(|ext| ext.token_data.as_ref());

                            let (tokens_vec, skip_token_annotation) = if let Some(tokens) =
                                token_data
                            {
                                // token_data skips the tokenizer, so bound its ids here.
                                ensure_token_ids_in_vocab(
                                    "nvext.token_data",
                                    tokens,
                                    self.token_id_bound,
                                )?;
                                tracing::info!(
                                    token_count = tokens.len(),
                                    first_tokens = ?&tokens[..std::cmp::min(5, tokens.len())],
                                    "[SIDECAR-SKIP-TOKENIZE] Found nvext.token_data — using pre-computed tokens, SKIPPING tokenization"
                                );
                                (tokens.clone(), true)
                            } else if has_backend_instance_id {
                                tracing::warn!(
                                    "backend_instance_id provided but no token_data; tokenizing prompt"
                                );
                                let encoding = self
                                    .encode_prompt_with_timing(
                                        formatted_prompt,
                                        raw_prompt.as_str(),
                                        tracker,
                                    )
                                    .await?;
                                (encoding.token_ids().to_vec(), false)
                            } else {
                                let encoding = self
                                    .encode_prompt_with_timing(
                                        formatted_prompt,
                                        raw_prompt.as_str(),
                                        tracker,
                                    )
                                    .await?;
                                (encoding.token_ids().to_vec(), false)
                            };

                            if request.has_annotation(ANNOTATION_TOKEN_IDS)
                                && !skip_token_annotation
                            {
                                annotations.insert(
                                    ANNOTATION_TOKEN_IDS.to_string(),
                                    serde_json::to_string(&tokens_vec)?,
                                );
                            }

                            tokens_out = tokens_vec;
                        }
                        TextInput::Batch(texts) => {
                            if texts.len() == 1 {
                                let encoding = self.encode_with_timing(&texts[0], tracker).await?;
                                let tokens = encoding.token_ids().to_vec();
                                tokens_out = tokens;
                            } else {
                                bail!(
                                    "Batch text input not supported for more than one text in requests (got {})",
                                    texts.len()
                                );
                            }
                        }
                    }
                }
            }
        }

        Self::capture_prompt_token_ids(request, tracker, &tokens_out);

        Ok((tokens_out, annotations))
    }

    /// Retain the authoritative rendered prompt only for clients that request
    /// the named response field. Ordinary requests do not clone the token list.
    fn capture_prompt_token_ids<R: NvExtProvider>(
        request: &R,
        tracker: Option<&RequestTracker>,
        token_ids: &[TokenIdType],
    ) {
        let requested = request
            .nvext()
            .and_then(|nvext| nvext.extra_fields.as_ref())
            .is_some_and(|fields| fields.iter().any(|field| field == "prompt_token_ids"));
        if requested && let Some(tracker) = tracker {
            tracker.set_prompt_token_ids(token_ids.to_vec());
        }
    }

    fn prompt_overflow_error(token_count: usize, combined_limit: usize) -> DynamoError {
        DynamoError::builder()
            .error_type(ErrorType::InvalidArgument)
            .message(format!(
                "This model's maximum context length is {} tokens. \
                 However, your messages resulted in {} tokens. \
                 Please reduce the length of the messages.",
                combined_limit, token_count,
            ))
            .public_details(PublicDetails::ContextLength {
                limit: combined_limit as u64,
                actual: Some(token_count as u64),
            })
            .build()
    }

    async fn encode_with_timing(
        &self,
        prompt: &str,
        tracker: Option<&RequestTracker>,
    ) -> anyhow::Result<Encoding> {
        let encode_start = Instant::now();
        // Offload the CPU-heavy BPE encode to the bounded blocking pool instead of running it on
        // the async event loop. For long prompts at high concurrency, a synchronous encode here
        // stalls the frontend tokio runtime for seconds, starving the I/O tasks that share the
        // runtime. Own the prompt + clone the tokenizer (Arc) so the closure is 'static + Send;
        // mirrors the embedding path's spawn_blocking offload.
        let owned = if prompt.contains('\0') {
            tracing::debug!("Prompt contains null bytes; stripping to avoid tokenizer divergence");
            prompt.replace('\0', "")
        } else {
            prompt.to_string()
        };
        let tokenizer = self.tokenizer.clone();
        let encoding = tokio::task::spawn_blocking(move || tokenizer.encode(&owned)).await??;
        if let Some(t) = tracker {
            t.record_tokenize_latency(encode_start.elapsed());
        }
        Ok(encoding)
    }

    async fn encode_prompt_with_timing(
        &self,
        formatted_prompt: Option<&RenderedPrompt>,
        raw_prompt: &str,
        tracker: Option<&RequestTracker>,
    ) -> anyhow::Result<Encoding> {
        let Some(prompt) = formatted_prompt
            .filter(|prompt| prompt.segments().is_some())
            .cloned()
        else {
            return self
                .encode_with_timing(
                    formatted_prompt
                        .map(RenderedPrompt::as_str)
                        .unwrap_or(raw_prompt),
                    tracker,
                )
                .await;
        };

        let encode_start = Instant::now();
        let tokenizer = self.tokenizer.clone();
        let encoding = tokio::task::spawn_blocking(move || {
            let segments = prompt
                .encode_segments()
                .expect("prompt was checked for rendered segments");
            tokenizer.encode_segments(&segments)
        })
        .await??;
        if let Some(t) = tracker {
            t.record_tokenize_latency(encode_start.elapsed());
        }
        Ok(encoding)
    }

    /// Preprocess an embedding request, handling both text and token ID inputs.
    ///
    /// For text inputs, tokenizes the text using the configured tokenizer.
    /// For token ID inputs, uses the provided token IDs directly and skips tokenization.
    ///
    /// Returns both the preprocessed request and a hashmap of annotations.
    pub async fn preprocess_embedding_request(
        &self,
        request: &NvCreateEmbeddingRequest,
    ) -> Result<(PreprocessedEmbeddingRequest, HashMap<String, String>)> {
        let _stage_guard = StageGuard::new(STAGE_PREPROCESS, "");
        let mut annotations = HashMap::new();
        let mut builder = PreprocessedEmbeddingRequest::builder();

        let embedding_tokenizers = self.embedding_tokenizers.as_ref().ok_or_else(|| {
            anyhow::anyhow!(
                "embedding tokenization is unavailable; construct the preprocessor with \
                 OpenAIPreprocessor::new_for_embeddings"
            )
        })?;
        let effective_add_special_tokens = request
            .add_special_tokens
            .or(embedding_tokenizers.add_special_tokens_default)
            .unwrap_or(true);
        let is_text_input = matches!(
            &request.inner.input,
            dynamo_protocols::types::EmbeddingInput::String(_)
                | dynamo_protocols::types::EmbeddingInput::StringArray(_)
        );
        let truncation_limit =
            self.embedding_truncation_limit(request.truncate_prompt_tokens, is_text_input)?;
        let mut all_token_ids = match &request.inner.input {
            dynamo_protocols::types::EmbeddingInput::String(s) => {
                let tokenizer = embedding_tokenizers.tokenizer(effective_add_special_tokens)?;
                let encoding = tokenizer.encode(s)?;
                vec![encoding.token_ids().to_vec()]
            }
            dynamo_protocols::types::EmbeddingInput::StringArray(arr) => {
                let tokenizer = embedding_tokenizers.tokenizer(effective_add_special_tokens)?;
                let input_strs: Vec<String> = arr.to_vec();
                let encodings = tokio::task::spawn_blocking({
                    let tokenizer = tokenizer.clone();
                    let strs = input_strs.clone();
                    move || {
                        tokenizer.encode_batch(&strs.iter().map(|s| s.as_str()).collect::<Vec<_>>())
                    }
                })
                .await??;
                encodings
                    .into_iter()
                    .map(|encoding| encoding.token_ids().to_vec())
                    .collect()
            }
            // Token input skips the tokenizer, so bound its ids here.
            dynamo_protocols::types::EmbeddingInput::IntegerArray(token_ids) => {
                ensure_token_ids_in_vocab("input", token_ids, self.token_id_bound)?;
                vec![token_ids.clone()]
            }
            dynamo_protocols::types::EmbeddingInput::ArrayOfIntegerArray(token_arrays) => {
                for token_ids in token_arrays {
                    ensure_token_ids_in_vocab("input", token_ids, self.token_id_bound)?;
                }
                token_arrays.clone()
            }
        };
        if let Some(limit) = truncation_limit {
            for token_ids in &mut all_token_ids {
                // This integration intentionally follows right truncation:
                // preserve the first N tokens.
                token_ids.truncate(limit);
            }
        }

        // Handle annotations
        if request.has_annotation(ANNOTATION_TOKEN_IDS) {
            annotations.insert(
                ANNOTATION_TOKEN_IDS.to_string(),
                serde_json::to_string(&all_token_ids)?,
            );
        }

        builder.token_ids(all_token_ids);
        builder.model(request.inner.model.clone());
        builder.encoding_format(request.inner.encoding_format.as_ref().map(|f| match f {
            EncodingFormat::Float => "float".to_string(),
            EncodingFormat::Base64 => "base64".to_string(),
        }));
        builder.truncate_prompt_tokens(request.truncate_prompt_tokens);
        builder.dimensions(request.inner.dimensions);

        builder.annotations(request.annotations().unwrap_or_default());
        builder.mdc_sum(Some(self.mdcsum.clone()));

        Ok((builder.build()?, annotations))
    }

    fn embedding_truncation_limit(
        &self,
        requested: Option<i64>,
        is_text_input: bool,
    ) -> Result<Option<usize>> {
        let Some(requested) = requested else {
            return Ok(None);
        };

        if requested < -1 {
            return Err(invalid_argument_error(format!(
                "truncate_prompt_tokens must be >= -1, got {requested}"
            )));
        }

        // Caller-supplied token IDs are already preprocessed; do not mutate them.
        if !is_text_input {
            return Ok(None);
        }

        let model_limit = self.context_length as usize;
        if requested == -1 {
            if model_limit == 0 {
                return Err(invalid_argument_error(
                    "truncate_prompt_tokens=-1 requires a configured model context length",
                ));
            }
            return Ok(Some(model_limit));
        }

        let requested = usize::try_from(requested).map_err(|_| {
            invalid_argument_error("truncate_prompt_tokens is too large for this platform")
        })?;
        if model_limit > 0 && requested > model_limit {
            return Err(invalid_argument_error(format!(
                "truncate_prompt_tokens={requested} cannot be greater than \
                 max_model_len={model_limit}. Please request a smaller truncation size."
            )));
        }

        Ok(Some(requested))
    }

    fn apply_unified_response_policies<S>(
        stream: S,
        emit_tool_calls: bool,
        defer_reasoning_for_nonempty_content: bool,
        reasoning_disabled: bool,
    ) -> Pin<Box<dyn Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send>>
    where
        S: Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send + 'static,
    {
        let stream: Pin<Box<dyn Stream<Item = _> + Send>> = Box::pin(
            Self::apply_tool_call_response_policy(stream, emit_tool_calls),
        );
        let stream = stream.map(move |mut response| {
            if reasoning_disabled && let Some(data) = response.data.as_mut() {
                for choice in &mut data.inner.choices {
                    if let Some(reasoning) = choice.delta.reasoning_content.take() {
                        let text = match choice.delta.content.take() {
                            Some(ChatCompletionMessageContent::Text(text)) => text,
                            None => String::new(),
                            Some(other) => {
                                choice.delta.content = Some(other);
                                choice.delta.reasoning_content = Some(reasoning);
                                continue;
                            }
                        };
                        choice.delta.content = Some(ChatCompletionMessageContent::Text(format!(
                            "{reasoning}{text}"
                        )));
                    }
                }
            }
            response
        });
        // Observe parser classification before force_nonempty deferral removes the
        // reasoning delta. The annotated usage trailer can still be held below until
        // every deferred recovery chunk has been emitted.
        let stream: Pin<Box<dyn Stream<Item = _> + Send>> =
            Box::pin(annotate_reasoning_usage(stream));
        let stream: Pin<Box<dyn Stream<Item = _> + Send>> = if defer_reasoning_for_nonempty_content
        {
            Box::pin(Self::defer_unified_reasoning_for_nonempty_content(stream))
        } else {
            stream
        };
        let stream: Pin<Box<dyn Stream<Item = _> + Send>> = if defer_reasoning_for_nonempty_content
        {
            Box::pin(Self::hold_usage_until_stream_end(stream))
        } else {
            stream
        };
        stream
    }

    fn tool_processing_route(
        &self,
        request: &NvCreateChatCompletionRequest,
        guided_tool_constraint: &crate::protocols::openai::GuidedToolConstraint,
    ) -> anyhow::Result<ToolProcessingRoute> {
        use crate::protocols::openai::chat_completions::{tool_parser_v2, unified_parser};

        let family = unified_parser::selected_request_family(
            self.tool_call_parser.as_deref(),
            self.runtime_config.reasoning_parser.as_deref(),
            request.inner.tool_choice.as_ref(),
            guided_tool_constraint,
        );
        // Options, capture, and aggregation reuse selection; only request routing
        // owns the operator-facing decision line.
        tracing::debug!(
            target: "dynamo_unified",
            tool_call_parser = ?self.tool_call_parser,
            reasoning_parser = ?self.runtime_config.reasoning_parser,
            ?family,
            version = ?tool_parser_v2::selected_version()?,
            "unified parser path decision"
        );
        if let Some(family) = family {
            return Ok(ToolProcessingRoute::Unified(family));
        }

        let uses_tool_call_structural_tag = guided_tool_constraint.uses_structural_tag();
        let selected_version = tool_parser_v2::selected_version()?;
        if selected_version == tool_parser_v2::ParserVersion::Auto
            && let Some(family) = unified_parser::configured_family(
                self.tool_call_parser.as_deref(),
                self.runtime_config.reasoning_parser.as_deref(),
            )
            && family == "muse_glimmer"
            && !uses_tool_call_structural_tag
            && matches!(
                request.inner.tool_choice.as_ref(),
                None | Some(ChatCompletionToolChoiceOption::Auto)
                    | Some(ChatCompletionToolChoiceOption::None)
            )
        {
            return Ok(ToolProcessingRoute::MuseUnified(family));
        }
        let effective_tool_call_parser = self.tool_call_parser.clone().or_else(|| {
            self.runtime_config
                .reasoning_parser
                .as_deref()
                .filter(|parser| matches!(*parser, "kimi_k3" | "kimi-k3"))
                .map(str::to_string)
        });
        let parser_unwraps_all_kimi_k3_responses = effective_tool_call_parser
            .as_deref()
            .is_some_and(|parser| matches!(parser, "kimi_k3" | "kimi-k3"));
        let tool_call_parsing_enabled = Self::tool_call_parsing_enabled(request);
        let has_tools = request.inner.has_effective_tools();
        let should_jail = if tool_call_parsing_enabled || parser_unwraps_all_kimi_k3_responses {
            Self::should_apply_tool_jail(
                effective_tool_call_parser.as_ref(),
                request.inner.tool_choice.as_ref(),
                has_tools,
            )?
        } else {
            false
        };

        if !should_jail {
            return Ok(ToolProcessingRoute::PassThrough);
        }

        tool_parser_v2::validate_tool_request_mode(
            selected_version,
            effective_tool_call_parser.as_deref(),
            self.runtime_config.reasoning_parser.as_deref(),
            should_jail,
        )
        .map_err(|error| invalid_argument_error(format!("{error:#}")))?;

        if let Some(parser_name) = effective_tool_call_parser.as_deref()
            && tool_parser_v2::enabled()
            && selected_version == tool_parser_v2::ParserVersion::V2
            && tool_parser_v2::supports_family(parser_name)
            && (uses_tool_call_structural_tag
                || matches!(
                    request.inner.tool_choice.as_ref(),
                    None | Some(ChatCompletionToolChoiceOption::Auto)
                ))
        {
            let parser_name = match parser_name {
                "deepseek-v4" | "deepseekv4" => "deepseek_v4",
                parser_name => parser_name,
            };
            return Ok(ToolProcessingRoute::ParserV2(parser_name.to_string()));
        }
        if selected_version == tool_parser_v2::ParserVersion::V2 {
            return Err(invalid_argument_error(format!(
                "{}=2 was requested, but this tool choice requires the v1 tool-call jail",
                env_llm::DYN_PARSER_VERSION
            )));
        }
        Ok(ToolProcessingRoute::LegacyJail(effective_tool_call_parser))
    }

    pub fn postprocessor_parsing_stream<S>(
        &self,
        stream: S,
        request: &NvCreateChatCompletionRequest,
        prompt_injected_reasoning: impl Into<PromptReasoningPrefill>,
        uses_tool_call_structural_tag: bool,
    ) -> anyhow::Result<
        impl Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send + 'static,
    >
    where
        S: Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send + 'static,
    {
        let guided_tool_constraint = crate::preprocessor::tool_choice::guided_tool_constraint(
            request,
            self.tool_call_parser.as_deref(),
            self.runtime_config.reasoning_parser.as_deref(),
            uses_tool_call_structural_tag,
        )?;
        let tool_processing_route = self.tool_processing_route(request, &guided_tool_constraint)?;
        self.postprocessor_parsing_stream_with_constraint(
            stream,
            request,
            prompt_injected_reasoning.into(),
            guided_tool_constraint,
            tool_processing_route,
        )
    }

    fn postprocessor_parsing_stream_with_constraint<S>(
        &self,
        stream: S,
        request: &NvCreateChatCompletionRequest,
        prompt_injected_reasoning: PromptReasoningPrefill,
        guided_tool_constraint: crate::protocols::openai::GuidedToolConstraint,
        tool_processing_route: ToolProcessingRoute,
    ) -> anyhow::Result<
        impl Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send + 'static,
    >
    where
        S: Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send + 'static,
    {
        use crate::protocols::openai::chat_completions::{tool_parser_v2, unified_parser};
        let prompt_prefill = prompt_injected_reasoning;
        let prompt_injected_reasoning = prompt_prefill.for_route(&tool_processing_route);
        let uses_tool_call_structural_tag = guided_tool_constraint.uses_structural_tag();
        let defer_reasoning_for_nonempty_content =
            Self::wants_reasoning_as_content_when_empty(request.chat_template_args.as_ref());
        // Two different streaming paths can release a grammar-constrained tool call
        // before its payload closes: the unified (v2) adapter below and the jail near
        // the end of this function. Both must obey the same rollback lever, so
        // `DYN_ENABLE_GUIDED_TOOL_STREAMING` is read ONCE, here, and the single decision
        // is handed to whichever path runs. Reading it again at either site would be a
        // second predicate that can drift; a site with no read at all makes the lever
        // silently inert for every request routed through it.
        let guided_tool_streaming = Self::guided_tool_streaming_release(
            guided_tool_constraint.installs_guided_json(),
            env_is_falsey(env_llm::DYN_ENABLE_GUIDED_TOOL_STREAMING),
        );

        if let ToolProcessingRoute::MuseUnified(family) = &tool_processing_route {
            let tool_definitions =
                crate::preprocessor::tool_choice::effective_tool_definitions(&request.inner)?;
            let tool_definitions = (!tool_definitions.is_empty()).then_some(tool_definitions);
            let reasoning_disabled = Self::request_disables_reasoning_with_family(
                request,
                self.tool_call_parser.as_deref(),
                self.runtime_config.reasoning_parser.as_deref(),
                None,
                Some(family),
            );
            let unified: Pin<Box<dyn Stream<Item = _> + Send>> =
                Box::pin(unified_parser::apply_stream_with_policy(
                    stream,
                    tool_definitions,
                    crate::protocols::openai::GuidedToolConstraint::None,
                    unified_parser::stream_prefill(family, prompt_injected_reasoning),
                    family,
                    guided_tool_streaming,
                    unified_parser::UnifiedRequestPolicy {
                        reasoning_disabled,
                        structured_response: Self::has_structured_response_format(request),
                    },
                ));
            return Ok(Self::apply_unified_response_policies(
                unified,
                Self::tool_call_parsing_enabled(request),
                defer_reasoning_for_nonempty_content,
                reasoning_disabled,
            ));
        }

        if let ToolProcessingRoute::Unified(family) = &tool_processing_route {
            let tool_definitions =
                crate::preprocessor::tool_choice::effective_tool_definitions(&request.inner)?;
            let tool_definitions = (!tool_definitions.is_empty()).then_some(tool_definitions);
            let reasoning_disabled = Self::request_disables_reasoning_with_family(
                request,
                self.tool_call_parser.as_deref(),
                self.runtime_config.reasoning_parser.as_deref(),
                None,
                Some(family),
            );
            let unified: Pin<Box<dyn Stream<Item = _> + Send>> =
                Box::pin(unified_parser::apply_stream_with_policy(
                    stream,
                    tool_definitions,
                    guided_tool_constraint,
                    unified_parser::StreamPrefill {
                        starting_state: unified_parser::stream_prefill(
                            family,
                            prompt_injected_reasoning,
                        ),
                        prefix: prompt_prefill.prefix,
                    },
                    family,
                    guided_tool_streaming,
                    unified_parser::UnifiedRequestPolicy {
                        reasoning_disabled,
                        structured_response: Self::has_structured_response_format(request),
                    },
                ));
            return Ok(Self::apply_unified_response_policies(
                unified,
                Self::tool_call_parsing_enabled(request),
                defer_reasoning_for_nonempty_content,
                reasoning_disabled,
            ));
        }

        // Guided output may be bare JSON or `reasoning</think>JSON`. Supported
        // parsers inspect the stream shape before deciding whether to parse it.
        let is_guided_tool_choice = matches!(
            request.inner.tool_choice,
            Some(ChatCompletionToolChoiceOption::Required)
                | Some(ChatCompletionToolChoiceOption::Named(_))
        );
        let is_structured_response = Self::has_structured_response_format(request);
        let is_guided_output = is_guided_tool_choice || is_structured_response;
        let reasoning_parser = self.runtime_config.reasoning_parser.as_deref();
        // Force parsers opt in by capability; prompt-seeded parsers opt in per request.
        // Structural-tag tool formats do not use this guided-JSON detection path.
        let inspect_force_reasoning_guided_output = is_guided_output
            && !uses_tool_call_structural_tag
            && Self::supports_reasoning_before_guided_json(reasoning_parser);
        let inspect_prompt_injected_guided_output = is_guided_output
            && prompt_injected_reasoning
            && !uses_tool_call_structural_tag
            && (Self::skips_guided_json_when_prompt_injected(reasoning_parser)
                || (is_structured_response
                    && Self::skips_structured_response_when_prompt_injected(reasoning_parser)));
        let inspect_unsupported_structured_response_reasoning_gate = is_structured_response
            && !uses_tool_call_structural_tag
            && !Self::structured_response_supports_sglang_reasoning_gate(reasoning_parser);
        let bypass_reasoning_for_bare_guided_json = inspect_force_reasoning_guided_output
            || inspect_prompt_injected_guided_output
            || inspect_unsupported_structured_response_reasoning_gate;
        // Preserve the legacy bypass for force-reasoning parsers not yet opted in.
        let skip_reasoning_for_guided_json = is_guided_output
            && !uses_tool_call_structural_tag
            && Self::is_force_reasoning_parser(reasoning_parser)
            && !inspect_force_reasoning_guided_output;

        let reasoning_disabled_by_request = Self::is_reasoning_disabled_by_request(
            self.runtime_config.reasoning_parser.as_deref(),
            request.chat_template_args.as_ref(),
        );

        // Try to parse reasoning content only if parser is configured.
        let should_parse_reasoning = self.runtime_config.reasoning_parser.is_some()
            && !reasoning_disabled_by_request
            && !skip_reasoning_for_guided_json;
        let should_strip_disabled_reasoning_start = reasoning_disabled_by_request
            && Self::is_nemotron_force_reasoning(self.runtime_config.reasoning_parser.as_deref())
            && !skip_reasoning_for_guided_json;
        let guided_reasoning_start_token =
            if should_parse_reasoning && bypass_reasoning_for_bare_guided_json {
                Self::guided_json_reasoning_start_token(reasoning_parser)
            } else {
                None
            };

        // Reasoning Content Parsing Transformation Step
        // Current Solution:
        // This step operates on Deltas created by the transform_postprocessor_stream function
        // Only access to text and not token_ids - so can not support parsing based on token_ids for now
        // Future Solution:
        // To address the limitation if needed in future: move this step before transform_postprocessor_stream and add new field of reasoning_content to the backend output
        // Use backend_output.reasoning_content field to fill out the deltas.
        // A bracket-prefixed reasoning opener is indistinguishable from JSON
        // until enough bytes arrive. Strip only a configured complete marker;
        // nonmatching bare JSON is restored unchanged for shape detection.
        let stream: Pin<Box<dyn Stream<Item = _> + Send>> =
            if let Some(start_token) = guided_reasoning_start_token {
                Box::pin(Self::strip_leading_reasoning_start_from_stream(
                    stream,
                    start_token,
                ))
            } else {
                Box::pin(stream)
            };

        // Only a force_nonempty_content request needs the deferral and the EOF
        // flush of a truncated `<think>` prefix; gating on the request keeps the
        // per-token clone and the finish_reasoning_stream() flush off every
        // other request's path. Same predicate the aggregator uses, so the
        // streaming and non-streaming paths cannot disagree.
        let stream: Pin<Box<dyn Stream<Item = _> + Send>> = if should_parse_reasoning {
            Box::pin(Self::parse_reasoning_content_from_stream_inner(
                stream,
                self.runtime_config.reasoning_parser.clone().unwrap(), // Safety: We already checked that parser is some, so gtg
                prompt_injected_reasoning,
                bypass_reasoning_for_bare_guided_json,
                defer_reasoning_for_nonempty_content,
            ))
        } else if should_strip_disabled_reasoning_start {
            Box::pin(Self::strip_leading_reasoning_start_from_stream(
                stream, "<think>",
            ))
        } else {
            Box::pin(stream)
        };
        let stream: Pin<Box<dyn Stream<Item = _> + Send>> =
            if defer_reasoning_for_nonempty_content || should_strip_disabled_reasoning_start {
                Box::pin(Self::hold_usage_until_stream_end(stream))
            } else {
                stream
            };
        let stream: Pin<Box<dyn Stream<Item = _> + Send>> = if should_parse_reasoning {
            Box::pin(annotate_reasoning_usage(stream))
        } else {
            stream
        };

        let tool_call_parsing_enabled = Self::tool_call_parsing_enabled(request);

        // Convert OpenAI tools to parser ToolDefinition format before applying jail
        let tool_definitions =
            crate::preprocessor::tool_choice::effective_tool_definitions(&request.inner)?;
        let tool_definitions = (!tool_definitions.is_empty()).then_some(tool_definitions);

        let transformed_stream: Pin<Box<dyn Stream<Item = _> + Send>> =
            match tool_processing_route {
                ToolProcessingRoute::ParserV2(parser_name) => Box::pin(
                    tool_parser_v2::apply_stream(stream, tool_definitions, parser_name),
                ),
                ToolProcessingRoute::LegacyJail(effective_tool_call_parser) => {
                    // A forced tool_choice installed a JSON grammar, so the jail may release
                    // calls as they arrive instead of buffering to the closing brace. The
                    // jail keeps its own native fallback, so a backend that ignores the
                    // grammar (MiniMax M2 emits XML under `required`) still parses normally.
                    // Same request-scoped decision the unified path above was given.
                    Box::pin(Self::apply_tool_calling_jail(
                        effective_tool_call_parser,
                        request.inner.tool_choice.clone(),
                        tool_definitions,
                        uses_tool_call_structural_tag,
                        guided_tool_streaming,
                        stream,
                    ))
                }
                ToolProcessingRoute::PassThrough => Box::pin(stream),
                ToolProcessingRoute::MuseUnified(_) | ToolProcessingRoute::Unified(_) => {
                    unreachable!("unified routes return before legacy response processing")
                }
            };

        Ok(Self::apply_tool_call_response_policy(
            transformed_stream,
            tool_call_parsing_enabled,
        ))
    }

    /// Enforce the request's tool-call policy after model-specific parsing.
    ///
    /// Kimi K3 must keep its jail active even when the request does not permit
    /// tools because the same parser unwraps ordinary XTML response channels.
    /// The jail can still emit structured tool-call deltas while doing that
    /// decoding, so parser activation alone is not an output-policy boundary.
    /// Apply the policy to the shared stream before the HTTP streaming and
    /// non-streaming paths diverge. This policy deliberately fails closed: when
    /// a decoder consumes a tool-only turn, suppressing the unauthorized call
    /// may leave an empty assistant turn with `finish_reason: stop`. Reconstructing
    /// parser-specific wire markup as assistant content would leak internal
    /// protocol tokens and could still be mistaken for an actionable call.
    fn apply_tool_call_response_policy<S>(
        stream: S,
        tool_call_parsing_enabled: bool,
    ) -> Pin<Box<dyn Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send + 'static>>
    where
        S: Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send + 'static,
    {
        if tool_call_parsing_enabled {
            return Box::pin(stream);
        }

        Box::pin(stream.map(|mut response| {
            if let Some(data) = response.data.as_mut() {
                for choice in &mut data.inner.choices {
                    choice.delta.tool_calls = None;
                    if choice.finish_reason
                        == Some(dynamo_protocols::types::FinishReason::ToolCalls)
                    {
                        choice.finish_reason = Some(dynamo_protocols::types::FinishReason::Stop);
                    }
                }
            }
            response
        }))
    }

    /// Ensure the first emitted delta for each choice carries the assistant role.
    ///
    /// This runs after reasoning and tool-call parsing so a parser that buffers the
    /// original role-bearing delta cannot leave downstream consumers without a role.
    fn normalize_chat_stream_roles<S>(
        stream: S,
    ) -> impl Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send + 'static
    where
        S: Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send + 'static,
    {
        let mut role_emitted_choices = HashSet::new();

        stream.map(move |mut response| {
            if let Some(data) = response.data.as_mut() {
                for choice in &mut data.inner.choices {
                    choice.delta.role = role_emitted_choices
                        .insert(choice.index)
                        .then_some(dynamo_protocols::types::Role::Assistant);
                }
            }
            response
        })
    }

    pub fn transform_postprocessor_stream<S, Resp>(
        stream: S,
        generator: Box<dyn DeltaGeneratorExt<Resp>>,
        context: Arc<dyn AsyncEngineContext>,
        emit_payload_usage_chunk: bool,
        trace_tokens_enabled: bool,
        trace_finish_reason_metadata: Option<crate::request_trace::SharedFinishReasonMetadata>,
        mm_counts: MultimodalCounts,
    ) -> impl Stream<Item = Annotated<Resp>> + Send
    where
        S: Stream<Item = Annotated<BackendOutput>> + Send + 'static,
        Resp: Send + Sync + Clone + 'static + std::fmt::Debug,
    {
        Self::transform_postprocessor_stream_with_image_tokens(
            stream,
            generator,
            context,
            emit_payload_usage_chunk,
            trace_tokens_enabled,
            trace_finish_reason_metadata,
            mm_counts,
            None,
        )
    }

    #[allow(clippy::too_many_arguments)]
    fn transform_postprocessor_stream_with_image_tokens<S, Resp>(
        stream: S,
        generator: Box<dyn DeltaGeneratorExt<Resp>>,
        context: Arc<dyn AsyncEngineContext>,
        emit_payload_usage_chunk: bool,
        trace_tokens_enabled: bool,
        trace_finish_reason_metadata: Option<crate::request_trace::SharedFinishReasonMetadata>,
        mm_counts: MultimodalCounts,
        image_tokens: Option<usize>,
    ) -> impl Stream<Item = Annotated<Resp>> + Send
    where
        S: Stream<Item = Annotated<BackendOutput>> + Send + 'static,
        Resp: Send + Sync + Clone + 'static + std::fmt::Debug,
    {
        struct DetokenizeMetricsGuard {
            tracker: Option<Arc<RequestTracker>>,
        }

        impl DetokenizeMetricsGuard {
            #[inline]
            fn tracker(&self) -> Option<&RequestTracker> {
                self.tracker.as_deref()
            }
        }

        impl Drop for DetokenizeMetricsGuard {
            fn drop(&mut self) {
                let Some(tracker) = self.tracker() else {
                    return;
                };
                if let Some(total) = tracker.detokenize_total_latency() {
                    DETOKENIZE_TOTAL_US.inc_by(total.as_micros() as f64);
                }
                DETOKENIZE_TOKEN_COUNT.inc_by(tracker.detokenize_count() as f64);
            }
        }

        struct State<Resp>
        where
            Resp: Send + Sync + Clone + 'static + std::fmt::Debug,
        {
            response_stream: Pin<Box<dyn Stream<Item = Annotated<BackendOutput>> + Send>>,
            response_generator: Box<dyn DeltaGeneratorExt<Resp>>,
            detokenize_metrics: DetokenizeMetricsGuard,
            context: Arc<dyn AsyncEngineContext>,
            cancelled: bool,
            cumulative_output_tokens: usize,
            finish_reason_sent: bool,
            usage_chunk_sent: bool,
            /// Buffered plain usage chunk to send to the client after the payload
            /// chunk (ANNOTATION_PAYLOAD_USAGE). Only Some when is_usage_enabled().
            pending_client_usage: Option<Annotated<Resp>>,
            finished: bool,
            emit_payload_usage_chunk: bool,
            trace_tokens_enabled: bool,
            trace_finish_reason_metadata: Option<crate::request_trace::SharedFinishReasonMetadata>,
            mm_counts: MultimodalCounts,
            image_tokens: Option<usize>,
        }

        let tracker = generator.tracker();
        let state = State {
            response_stream: Box::pin(stream),
            response_generator: generator,
            detokenize_metrics: DetokenizeMetricsGuard { tracker },
            context: context.clone(),
            cancelled: false,
            cumulative_output_tokens: 0,
            finish_reason_sent: false,
            usage_chunk_sent: false,
            pending_client_usage: None,
            finished: false,
            emit_payload_usage_chunk,
            trace_tokens_enabled,
            trace_finish_reason_metadata,
            mm_counts,
            image_tokens,
        };

        // transform the common response stream into a chat response stream

        stream::unfold(state, |mut inner| {
            async move {
                // Drain the buffered client-facing plain usage chunk first.
                // This MUST come before the `finished` guard: the stream-end
                // handler sets inner.finished = true before returning the payload
                // chunk, so on the very next iteration the finished guard would
                // terminate before we ever emit the client chunk.
                if let Some(client_chunk) = inner.pending_client_usage.take() {
                    inner.finished = true;
                    // Emit unconditionally to match the non-payload path below; the
                    // chunk is only buffered after a finish_reason, so payload capture must
                    // not alter the client SSE tail.
                    return Some((client_chunk, inner));
                }

                // If already finished (and no pending client chunk), stop.
                if inner.finished {
                    return None;
                }

                if let Some(mut response) = inner.response_stream.next().await {
                    // Split topology: overlay a standalone router's forwarded routing_data
                    // (timing, query-only token_ids) onto this request's tracker so the
                    // frontend's nvext/timing surfaces populate.
                    drain_router_routing_data(
                        &mut response.data,
                        inner.detokenize_metrics.tracker(),
                    );

                    if inner.cancelled {
                        tracing::debug!(
                            request_id = inner.context.id(),
                            "Cancellation issued last message; closing stream"
                        );
                        // inner.finished = true; // Mark as finished
                        return None;
                    }

                    tracing::trace!(
                        request_id = inner.context.id(),
                        "Processing common response: {:?}",
                        response
                    );

                    // Check if this response has a finish_reason
                    let has_finish_reason = response
                        .data
                        .as_ref()
                        .map(|d| d.finish_reason.is_some())
                        .unwrap_or(false);

                    let (chunk_tokens, isl) = if let Some(ref backend_output) = response.data {
                        let chunk_tokens = backend_output.token_ids.len();
                        inner.cumulative_output_tokens += chunk_tokens;

                        let isl = inner.response_generator.get_isl().map(|isl| isl as usize);

                        crate::request_trace::record_backend_finish_reason_metadata(
                            inner.trace_finish_reason_metadata.as_ref(),
                            backend_output.index,
                            backend_output.finish_reason.as_ref(),
                            backend_output.stop_reason.as_ref(),
                        );

                        (chunk_tokens, isl)
                    } else {
                        (0, None)
                    };

                    let current_osl = inner.cumulative_output_tokens;

                    let mut response = response.map_data(|data| {
                        inner
                            .response_generator
                            .choice_from_postprocessor(data)
                            .inspect_err(|e| {
                                tracing::error!(
                                    request_id = inner.context.id(),
                                    "Error processing common response: {:?}",
                                    e
                                );
                                inner.cancelled = true;
                                inner.context.stop_generating();
                            })
                            .map_err(|e| e.to_string())
                    });

                    // Create LLM metrics annotation with prefill/decode worker info from tracker.
                    // Worker types are stored at routing time to avoid expensive MDC lookup.
                    let tracker = inner.detokenize_metrics.tracker();
                    let llm_metrics = build_llm_metric_annotation(
                        tracker,
                        isl.unwrap_or(0),
                        current_osl,
                        chunk_tokens,
                        None,
                        inner.mm_counts,
                        inner.image_tokens,
                    );
                    if inner.trace_tokens_enabled {
                        crate::request_trace::record_llm_metric_tokens(
                            tracker,
                            isl,
                            current_osl,
                            None,
                        );
                    }

                    attach_llm_metrics(&mut response, llm_metrics);

                    // Mark if we've seen a finish_reason
                    if has_finish_reason {
                        inner.finish_reason_sent = true;
                    }

                    tracing::trace!(
                        request_id = inner.context.id(),
                        "OpenAI NvCreateChatCompletionStreamResponse: {:?}",
                        response
                    );

                    Some((response, inner))
                } else {
                    // Stream has ended - must set finished to true to prevent unfold from polling
                    // again. The stream is exhausted and will panic if polled after None.
                    inner.finished = true;

                    if inner.finish_reason_sent && !inner.usage_chunk_sent {
                        inner.usage_chunk_sent = true;

                        let usage_chunk = inner.response_generator.create_usage_chunk();
                        let usage = inner.response_generator.get_usage();
                        let tracker = inner.detokenize_metrics.tracker();
                        let cached_tokens = usage
                            .prompt_tokens_details
                            .as_ref()
                            .and_then(|d| d.cached_tokens.map(|c| c as usize));
                        let llm_metrics = build_llm_metric_annotation(
                            tracker,
                            usage.prompt_tokens as usize,
                            usage.completion_tokens as usize,
                            0,
                            cached_tokens,
                            inner.mm_counts,
                            inner.image_tokens,
                        );
                        if inner.trace_tokens_enabled {
                            crate::request_trace::record_llm_metric_tokens(
                                tracker,
                                Some(usage.prompt_tokens as usize),
                                usage.completion_tokens as usize,
                                cached_tokens,
                            );
                        }

                        let usage_requested = inner.response_generator.is_usage_enabled();

                        if inner.emit_payload_usage_chunk {
                            // Payload capture on: emit a dedicated payload-usage chunk that
                            // always carries usage for the payload DeltaAggregator
                            // (the EventConverter strips it entirely from the
                            // client), and buffer the plain client usage chunk only
                            // when include_usage was requested.
                            if usage_requested {
                                inner.pending_client_usage = Some(Annotated::<Resp> {
                                    id: None,
                                    data: Some(usage_chunk.clone()),
                                    event: None,
                                    comment: None,
                                    error: None,
                                });
                            }

                            let annotation =
                                llm_metrics.to_annotation::<()>().unwrap_or_else(|e| {
                                    tracing::warn!("Failed to serialize metrics: {}", e);
                                    Annotated::<()>::from_data(())
                                });
                            let payload_usage = Annotated::<Resp> {
                                id: None,
                                data: Some(usage_chunk),
                                event: Some(ANNOTATION_PAYLOAD_USAGE.to_string()),
                                comment: annotation.comment,
                                error: None,
                            };
                            Some((payload_usage, inner))
                        } else {
                            // Payload capture off: a single usage chunk; data is present only
                            // when include_usage was requested. Metrics ride via the
                            // typed (serde-skip) llm_metrics field for internal
                            // observation, never reaching the client.
                            let data = if usage_requested {
                                Some(usage_chunk)
                            } else {
                                None
                            };
                            let mut annotated_usage = Annotated::<Resp> {
                                id: None,
                                data,
                                event: None,
                                comment: None,
                                error: None,
                            };
                            attach_llm_metrics(&mut annotated_usage, llm_metrics);
                            Some((annotated_usage, inner))
                        }
                    } else {
                        // stream closed
                        None
                    }
                }
            }
        })
        .fuse()
    }

    /// Transform engine embedding output stream to OpenAI embedding response stream
    pub fn transform_embedding_postprocessor_stream<S>(
        stream: S,
        original_request: NvCreateEmbeddingRequest,
    ) -> impl Stream<Item = Annotated<NvCreateEmbeddingResponse>> + Send
    where
        S: Stream<Item = Annotated<EmbeddingsEngineOutput>> + Send + 'static,
    {
        // The worker always returns base64-encoded little-endian f32 bytes.
        // Pass base64 through, or decode to floats (the public default).
        let encode_base64 = matches!(
            original_request.inner.encoding_format,
            Some(dynamo_protocols::types::EncodingFormat::Base64)
        );
        stream.map(move |output| {
            output.map_data(|engine_output| {
                if engine_output.embeddings.is_empty() {
                    return Err("embedding worker returned an empty `embeddings` field".to_string());
                }
                let embeddings: Vec<dynamo_protocols::types::Embedding> = engine_output
                    .embeddings
                    .into_iter()
                    .enumerate()
                    .map(|(index, encoded)| {
                        let value = if encode_base64 {
                            dynamo_protocols::types::EmbeddingVector::Base64(encoded)
                        } else {
                            dynamo_protocols::types::EmbeddingVector::Float(
                                decode_base64_to_floats(&encoded)?,
                            )
                        };
                        Ok::<_, String>(dynamo_protocols::types::Embedding {
                            index: index as u32,
                            object: "embedding".to_string(),
                            embedding: value,
                        })
                    })
                    .collect::<Result<Vec<_>, String>>()?;

                let response = NvCreateEmbeddingResponse {
                    inner: dynamo_protocols::types::CreateEmbeddingResponse {
                        object: "list".to_string(),
                        model: original_request.inner.model.clone(),
                        data: embeddings,
                        usage: dynamo_protocols::types::EmbeddingUsage {
                            prompt_tokens: engine_output.prompt_tokens,
                            total_tokens: engine_output.total_tokens,
                        },
                    },
                };

                Ok(response)
            })
        })
    }

    /// Determine if we should apply the tool calling jail based on configuration
    /// Returns Ok(true) if jail should be applied, Ok(false) if not, or Err if invalid config
    pub fn should_apply_tool_jail(
        tool_call_parser: Option<&String>,
        tool_choice: Option<&ChatCompletionToolChoiceOption>,
        has_tools: bool,
    ) -> std::result::Result<bool, Error> {
        // Necessary for now because K3 needs a special parser for XTML channel
        // parsing on all responses, regardless of tool or reasoning usage.
        // Keep its parser active so response/message wrappers never leak into
        // OpenAI `content`, even when the request has no tools.
        if tool_call_parser.is_some_and(|parser| matches!(parser.as_str(), "kimi_k3" | "kimi-k3")) {
            return Ok(true);
        }

        match (tool_call_parser, tool_choice, has_tools) {
            // tool_choice=required/named work without parser (use Immediate jail mode)
            (None, Some(ChatCompletionToolChoiceOption::Required), true) => Ok(true),
            (None, Some(ChatCompletionToolChoiceOption::Named(_)), true) => Ok(true),

            // tool_choice=auto requires a parser
            (None, Some(ChatCompletionToolChoiceOption::Auto), true) => {
                tracing::warn!(
                    "Tool choice 'auto' specified but no tool parser configured; proceeding without jailing"
                );
                Ok(false)
            }

            // Parser exists and tools might be called
            (Some(_), Some(ChatCompletionToolChoiceOption::None), _) => {
                Ok(false) // Explicitly disabled
            }
            (Some(_), Some(_), true) => Ok(true), // Any other tool_choice with tools
            (Some(_), None, true) => Ok(true),    // Default behavior when tools present

            // No tools or no parser
            _ => Ok(false),
        }
    }

    /// Whether a forced tool_choice's installed JSON grammar should release
    /// calls incrementally, or fall back to buffer-to-completion.
    ///
    /// The SINGLE owner of that decision, for BOTH streaming paths that can act on
    /// it: the tool-calling jail (`apply_tool_calling_jail`'s `guided_streaming`)
    /// and the unified v2 adapter (`unified_parser::apply_stream_with_constraint`'s
    /// `guided_streaming`, which becomes `StreamBestEffort` vs `RecoverAsText`).
    /// `postprocessor_parsing_stream_with_constraint` calls this once per request
    /// and hands the answer to whichever path runs; neither site re-reads the env
    /// var, because two reads are two predicates that can drift.
    ///
    /// Rollback lever: the grammar-constrained decoding itself lives in the
    /// published `dynamo-parsers` / `dynamo-parsers-v2` dependencies, not in this
    /// repo, so a backend that misbehaves under guided decoding in production has
    /// no same-release fix other than `DYN_ENABLE_GUIDED_TOOL_STREAMING`. On by
    /// default; set it to a falsy value (`0`/`false`) to fall back to
    /// buffer-to-completion.
    fn guided_tool_streaming_release(installs_guided_json: bool, rollback_disabled: bool) -> bool {
        installs_guided_json && !rollback_disabled
    }

    /// Apply tool calling jail to the stream if needed.
    ///
    /// The jail itself now lives in `dynamo-parsers`
    /// (`dynamo_parsers::tool_calling::jail`), where it operates on the shared
    /// `CreateChatCompletionStreamResponse` — dynamo-parsers cannot depend on
    /// dynamo-runtime, so it does not know about `Annotated` or the `Nv`
    /// newtype. This method is the boundary adapter: it unwraps the dynamo
    /// `Annotated<Nv{inner, nvext}>` stream into the jail's
    /// `Annotated<CreateChatCompletionStreamResponse>`, runs the moved jail, and
    /// re-wraps the result.
    ///
    /// The parser can buffer and rewrite several input chunks before it emits an
    /// output. Completion token IDs therefore describe the ordered buffered
    /// group, not the rewritten text in one output delta.
    pub fn apply_tool_calling_jail<S>(
        tool_call_parser: Option<String>,
        tool_choice: Option<dynamo_protocols::types::ChatCompletionToolChoiceOption>,
        tool_definitions: Option<Vec<dynamo_parsers::tool_calling::ToolDefinition>>,
        uses_tool_call_structural_tag: bool,
        guided_streaming: bool,
        stream: S,
    ) -> impl Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send
    where
        S: Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send + 'static,
    {
        use dynamo_parsers::tool_calling::jail::{
            Annotated as JailAnnotated, apply_tool_calling_jail,
            apply_tool_calling_jail_with_guided_streaming,
        };
        use std::sync::{Arc, Mutex};

        // The jail operates on the shared `Create` payload and never touches the
        // dynamo-only typed `llm_metrics`, which `transform_postprocessor_stream`
        // stamps *upstream* of the jail. `Create` has no slot for it, so buffer it
        // here on the way in and re-attach on the way out — keeping the metrics off
        // the shared type while preserving them across the boundary.
        //
        // `llm_metrics` is cumulative (`output_tokens`) plus per-chunk
        // (`chunk_tokens`), and the jail may fold N buffered input chunks into one
        // emitted chunk. So accumulate `chunk_tokens` and stamp the running total,
        // with the latest cumulative fields, onto the next emitted data chunk. That
        // preserves what `metrics.rs` records: `observe_response` sums `chunk_tokens`
        // and `observe_current_osl` takes the latest `output_tokens`. (The
        // annotation form on data-less usage chunks rides through untouched via
        // `event`/`comment`.)
        //
        // `nvext` uses the unary aggregator's merge rules: completion token IDs
        // are appended, while the latest supplied value wins for every other
        // top-level field. `engine_data` is replaced as one complete value.
        #[derive(Default)]
        struct PendingDynamoMetadata {
            metrics_template: Option<LLMMetricAnnotation>,
            chunk_tokens: usize,
            nvext: Option<serde_json::Value>,
            prompt_logprobs: Option<Arc<crate::protocols::common::llm_backend::PromptLogprobs>>,
            response_template: Option<dynamo_protocols::types::CreateChatCompletionStreamResponse>,
        }
        let pending = Arc::new(Mutex::new(PendingDynamoMetadata::default()));
        let pending_in = Arc::clone(&pending);

        // The legacy jail recognizes markers with a substring search. Retain
        // GLM47 text per choice so the terminal chunk can use the shared
        // quote-aware marker policy before exposing content to the client.
        #[derive(Default)]
        struct ChoiceRecovery {
            input_text: String,
            emitted_text: String,
        }
        let is_glm47 = tool_call_parser.as_deref() == Some("glm47");
        let glm47_config = dynamo_parsers::tool_calling::config::Glm47ParserConfig::default();
        let glm47_start = glm47_config.tool_call_start;
        let glm47_end = glm47_config.tool_call_end;
        let choice_recovery: Arc<Mutex<std::collections::HashMap<u32, ChoiceRecovery>>> =
            Arc::new(Mutex::new(std::collections::HashMap::new()));
        let choice_recovery_in = Arc::clone(&choice_recovery);
        let glm47_start_in = glm47_start.clone();
        let glm47_end_in = glm47_end.clone();

        // The jail's own (vendored, out-of-scope) finalize logic cannot tell an
        // error-terminated input stream from one that genuinely completed — it
        // sees a plain EOF either way — so on a natural EOF it can synthesize a
        // `tool_calls` finish chunk from whatever partial arguments it had
        // buffered, even though the request actually failed upstream. Both
        // unified adapters (`unified_parser::apply_stream_with_constraint`,
        // `tool_parser_v2::apply_stream`/`apply_unified_stream`) already give a
        // terminal upstream error this exact contract: `yield response; return;`,
        // dropping everything after. Give the jail wrapper the same contract:
        // `terminal_error` latches the first error `Annotated` observed on the
        // way in, `take_while` below stops it from ever reaching the jail (so
        // the jail's finalize never runs on an error-caused EOF at all), and the
        // matching `take_while`/`chain` on the way out (below) drops anything
        // the jail still emits after that point and substitutes the error
        // instead — never letting a synthesized completion reach the caller.
        let terminal_error: Arc<Mutex<Option<Annotated<NvCreateChatCompletionStreamResponse>>>> =
            Arc::new(Mutex::new(None));
        let terminal_error_in = Arc::clone(&terminal_error);
        let stream = stream.take_while(move |a| {
            let is_error = a.is_error();
            if is_error {
                *terminal_error_in
                    .lock()
                    .expect("jail terminal error poisoned") = Some(a.clone());
            }
            std::future::ready(!is_error)
        });

        // dynamo `Annotated<Nv>` -> jail `Annotated<Create>` (buffer Dynamo metadata)
        let jail_input = stream.map(move |mut a| {
            let has_metadata = a.data.as_ref().is_some_and(|nv| {
                nv.llm_metrics.is_some() || nv.nvext.is_some() || nv.prompt_logprobs.is_some()
            });
            if has_metadata {
                let mut p = pending_in
                    .lock()
                    .expect("jail Dynamo metadata buffer poisoned");
                if let Some(nv) = a.data.as_mut() {
                    if p.response_template.is_none() {
                        p.response_template = Some(
                            dynamo_protocols::types::CreateChatCompletionStreamResponse {
                                id: nv.inner.id.clone(),
                                object: nv.inner.object.clone(),
                                created: nv.inner.created,
                                model: nv.inner.model.clone(),
                                choices: Vec::new(),
                                usage: None,
                                service_tier: nv.inner.service_tier.clone(),
                                system_fingerprint: nv.inner.system_fingerprint.clone(),
                            },
                        );
                    }
                    if let Some(metrics) = nv.llm_metrics.take() {
                        p.chunk_tokens = p.chunk_tokens.saturating_add(metrics.chunk_tokens);
                        p.metrics_template = Some(metrics);
                    }
                    merge_response_nvext(&mut p.nvext, nv.nvext.take());
                    if let Some(prompt_logprobs) = nv.prompt_logprobs.take() {
                        p.prompt_logprobs = Some(prompt_logprobs);
                    }
                }
            }
            if is_glm47 && let Some(data) = &a.data {
                let mut recovery = choice_recovery_in
                    .lock()
                    .expect("choice recovery buffer poisoned");
                for choice in &data.inner.choices {
                    if let Some(ChatCompletionMessageContent::Text(content)) = &choice.delta.content
                    {
                        let state = recovery.entry(choice.index).or_default();
                        state.input_text.push_str(content);
                        // A completed call is already owned by the jail. Retain only
                        // the suffix after it, so terminal recovery examines the
                        // final unfinished call while the shared scanner decides
                        // whether each opener is real or quoted prose.
                        while let Some(marker_start) = crate::protocols::openai::chat_completions::unified_parser::first_unquoted_native_tool_call_marker(&state.input_text, "glm47") {
                            let after_marker =
                                &state.input_text[marker_start + glm47_start_in.len()..];
                            let Some(end) = after_marker.find(&glm47_end_in) else {
                                break;
                            };
                            state.input_text.drain(
                                ..marker_start
                                    + glm47_start_in.len()
                                    + end
                                    + glm47_end_in.len(),
                            );
                            state.emitted_text.clear();
                        }
                        // Text the client already holds cannot change what terminal
                        // recovery emits, so drop it and keep the buffer proportional
                        // to what is still pending. Without this the buffer grows for
                        // the whole response and every chunk rescans all of it.
                        //
                        // A quote character is the exception: the marker scanner reads
                        // `"<tool_call>"` as prose, and it can only know that from the
                        // quote to its left. Dropping a quote-free prefix cannot change
                        // any later verdict, so that is the only prefix dropped here.
                        // A response that quotes on every chunk keeps the old growth.
                        if !state.emitted_text.is_empty()
                            && !state.input_text.contains(['"', '\'', '`'])
                            && crate::protocols::openai::chat_completions::unified_parser::unquoted_native_tool_call_marker_or_prefix_start(&state.input_text, "glm47").is_none()
                            && let Some(unemitted) =
                                state.input_text.strip_prefix(state.emitted_text.as_str())
                        {
                            state.input_text = unemitted.to_string();
                            state.emitted_text.clear();
                        }
                    }
                }
            }
            debug_assert!(a.error.is_none(), "terminal errors must bypass the jail");
            JailAnnotated {
                data: a.data.map(|nv| nv.inner),
                id: a.id,
                event: a.event,
                comment: a.comment,
                // Terminal errors were removed by `take_while` above and are
                // chained back as the original typed Dynamo annotation below.
                error: None,
            }
        });

        // jail `Annotated<Create>` -> dynamo `Annotated<Nv>` (re-attach Dynamo metadata)
        // The crate encodes the opt-in in WHICH entry point you call, so pick here and
        // box both arms to one type. `apply_tool_calling_jail` keeps the published
        // five-argument signature for everyone else.
        let jailed: Pin<
            Box<
                dyn Stream<
                        Item = JailAnnotated<
                            dynamo_protocols::types::CreateChatCompletionStreamResponse,
                        >,
                    > + Send,
            >,
        > = if guided_streaming {
            Box::pin(apply_tool_calling_jail_with_guided_streaming(
                tool_call_parser,
                tool_choice,
                tool_definitions,
                uses_tool_call_structural_tag,
                jail_input,
            ))
        } else {
            Box::pin(apply_tool_calling_jail(
                tool_call_parser,
                tool_choice,
                tool_definitions,
                uses_tool_call_structural_tag,
                jail_input,
            ))
        };
        let pending_out = Arc::clone(&pending);
        let pending_eof = Arc::clone(&pending);
        let jailed_output = jailed.flat_map(move |a| {
            debug_assert!(
                a.error.is_none(),
                "dynamo-parsers must not construct errors"
            );
            // Metrics can ride on payload-only usage chunks because the HTTP
            // layer observes them before removing the chunk. Client-visible
            // nvext must wait for a non-payload-usage output with a choice.
            let has_choices = a.data.as_ref().is_some_and(|data| !data.choices.is_empty());
            let is_payload_usage = a.event.as_deref() == Some(ANNOTATION_PAYLOAD_USAGE);
            let mut prompt_logprobs = None;
            let (llm_metrics, nvext) = a.data.as_ref().map_or((None, None), |_| {
                let mut p = pending_out
                    .lock()
                    .expect("jail Dynamo metadata buffer poisoned");
                let chunk_tokens = p.chunk_tokens;
                p.chunk_tokens = 0;
                let metrics = p.metrics_template.take().map(|mut metrics| {
                    metrics.chunk_tokens = chunk_tokens;
                    metrics
                });
                let nvext = if has_choices && !is_payload_usage {
                    prompt_logprobs = p.prompt_logprobs.take();
                    p.nvext.take()
                } else {
                    None
                };
                (metrics, nvext)
            });
            let mut nv_chunk = Annotated {
                data: a.data.map(|inner| NvCreateChatCompletionStreamResponse {
                    inner,
                    nvext,
                    prompt_logprobs,
                    llm_metrics,
                    tool_call_completion: Vec::new(),
                }),
                id: a.id,
                event: a.event,
                comment: a.comment,
                // dynamo-parsers never constructs errors; Dynamo's original
                // typed terminal annotation bypasses this conversion.
                error: None,
            };

            if is_glm47 && let Some(data) = &mut nv_chunk.data {
                let mut recovery = choice_recovery
                    .lock()
                    .expect("choice recovery buffer poisoned");
                for choice in &mut data.inner.choices {
                    let state = recovery.entry(choice.index).or_default();
                    if let Some(marker_start) = crate::protocols::openai::chat_completions::unified_parser::unquoted_native_tool_call_marker_or_prefix_start(&state.input_text, "glm47") {
                        let desired_content = &state.input_text[..marker_start];
                        // An EOS inside a tool call is incomplete even if the engine reports stop.
                        let dropped_call_reported_as_length = choice.finish_reason
                            == Some(dynamo_protocols::types::FinishReason::Stop)
                            && choice.delta.tool_calls.is_none()
                            && crate::protocols::openai::chat_completions::unified_parser::first_unquoted_native_tool_call_marker(&state.input_text, "glm47").is_some();
                        if dropped_call_reported_as_length {
                            tracing::warn!(
                                choice_index = choice.index,
                                why = "dropped_native_tool_call_reported_as_length",
                                dropped_bytes = state.input_text.len() - desired_content.len(),
                                "glm47 streaming: reporting length instead of stop for a tool call dropped at end of stream"
                            );
                            choice.finish_reason =
                                Some(dynamo_protocols::types::FinishReason::Length);
                        }
                        if choice.finish_reason
                            == Some(dynamo_protocols::types::FinishReason::Length)
                            && crate::protocols::openai::chat_completions::unified_parser::first_unquoted_native_tool_call_marker(&state.input_text, "glm47").is_some()
                        {
                            // Count EOS drops separately from max_tokens truncation.
                            if !dropped_call_reported_as_length {
                                tracing::warn!(
                                    choice_index = choice.index,
                                    why = "truncated_native_tool_call_suppressed",
                                    suppressed_bytes = state.input_text.len() - desired_content.len(),
                                    "glm47 streaming: suppressing incomplete native tool output on length finish"
                                );
                            }
                            let replacement = desired_content
                                .strip_prefix(&state.emitted_text)
                                .unwrap_or_default();
                            choice.delta.content = (!replacement.is_empty()).then(|| {
                                ChatCompletionMessageContent::Text(replacement.to_string())
                            });
                        }
                    } else if choice.finish_reason
                        == Some(dynamo_protocols::types::FinishReason::Length)
                        && choice.delta.tool_calls.is_none()
                    {
                        let replacement = state
                            .input_text
                            .strip_prefix(&state.emitted_text)
                            .unwrap_or_default();
                        choice.delta.content = (!replacement.is_empty())
                            .then(|| ChatCompletionMessageContent::Text(replacement.to_string()));
                    }

                    if let Some(ChatCompletionMessageContent::Text(content)) = &choice.delta.content {
                        state.emitted_text.push_str(content);
                    }
                }
            }

            futures::stream::iter(std::iter::once(nv_chunk))
        });

        // Once an upstream error is latched, drop any output the jail synthesized
        // while it observed the shortened stream. The wrapper below then emits only
        // the original error and discards all pending metadata and usage.
        let terminal_error_out = Arc::clone(&terminal_error);
        let jailed_output = jailed_output.take_while(move |_| {
            let stop = terminal_error_out
                .lock()
                .expect("jail terminal error poisoned")
                .is_some();
            std::future::ready(!stop)
        });

        let with_eof_metadata = async_stream::stream! {
            tokio::pin!(jailed_output);
            while let Some(response) = jailed_output.next().await {
                yield response;
            }

            let terminal_error = {
                terminal_error
                    .lock()
                    .expect("jail terminal error poisoned")
                    .take()
            };
            if let Some(error) = terminal_error {
                {
                    let mut p = pending_eof
                        .lock()
                        .expect("jail Dynamo metadata buffer poisoned");
                    p.metrics_template = None;
                    p.chunk_tokens = 0;
                    p.nvext = None;
                    p.prompt_logprobs = None;
                    p.response_template = None;
                }
                yield error;
                return;
            }

            let eof_metadata = {
                let mut p = pending_eof
                    .lock()
                    .expect("jail Dynamo metadata buffer poisoned");
                let chunk_tokens = p.chunk_tokens;
                p.chunk_tokens = 0;
                let llm_metrics = p.metrics_template.take().map(|mut metrics| {
                    metrics.chunk_tokens = chunk_tokens;
                    metrics
                });
                let nvext = p.nvext.take();
                let prompt_logprobs = p.prompt_logprobs.take();
                if llm_metrics.is_none() && nvext.is_none() && prompt_logprobs.is_none() {
                    None
                } else {
                    p.response_template.take().map(|inner| Annotated {
                        data: Some(NvCreateChatCompletionStreamResponse {
                            inner,
                            nvext,
                            prompt_logprobs,
                            llm_metrics,
                            tool_call_completion: Vec::new(),
                        }),
                        id: None,
                        event: None,
                        comment: None,
                        error: None,
                    })
                }
            };
            if let Some(response) = eof_metadata {
                yield response;
            }
        };

        Self::hold_usage_until_stream_end(with_eof_metadata)
    }

    /// Whether the selected tool-call or reasoning parser depends on the
    /// engine emitting special tokens (e.g. Gemma 4's `<|tool_call>` /
    /// `<|channel>`). Mirrors upstream vLLM's per-parser `adjust_request`
    /// hooks. Used to flip the request default for `skip_special_tokens`
    /// from `true` to `false` so the parsers actually see the markers
    /// they're matching on.
    fn parser_requires_special_tokens(
        tool_call_parser: Option<&str>,
        reasoning_parser: Option<&str>,
    ) -> Result<bool> {
        if let Some(family) =
            crate::protocols::openai::chat_completions::unified_parser::selected_family(
                tool_call_parser,
                reasoning_parser,
            )
        {
            // The unified grammar owns token preservation, including native tool markers.
            return dynamo_parsers_v2::create_unified_parser_for_family(family, &[])
                .with_context(|| {
                    format!("Failed to resolve special-token requirements for {family}")
                })
                .map(|parser| parser.preserve_special_tokens());
        }
        // Parsers in this allow-list match against special tokens that the
        // tokenizer would otherwise strip when `skip_special_tokens=true`
        // (the OpenAI-API default). Without the tokens preserved through
        // decode the parsers silently produce empty reasoning_content /
        // tool_calls.
        //
        // - gemma4: `<|think|>` prompt trigger plus parser-visible
        //   `<|channel>` / `<channel|>` reasoning markers and tool-call markers.
        // - harmony / gpt_oss: `<|channel|>analysis<|message|>...<|end|>`.
        // - kimi_k2: `<|tool_calls_section_begin|>` / `<|tool_calls_section_end|>`.
        // - kimi_k25: `</think>` (special token id 163607).
        // - kimi_k3: `<|open|>` / `<|close|>` / `<|sep|>` XTML markers.
        // - mistral: `[THINK]` / `[/THINK]` reasoning markers.
        // - minimax_m3: `]<]minimax[>[` tool-call namespace tokens and
        //   `<mm:think>` reasoning markers.
        // - inkling: `<|message_model|>` / `<|content_thinking|>` /
        //   `<|content_text|>` / `<|content_invoke_tool_json|>` / `<|end_message|>`
        //   channel markers, consumed by both the tool-call and reasoning parsers.
        // - muse_glimmer: `<|start|>` / `<|message|>` / `<|eom|>` / `<|eot|>`
        //   channel markers, consumed by the unified parser (reasoning + content +
        //   tool calls); matched on either parser name since the card may set only
        //   the reasoning name.
        Ok(matches!(
            tool_call_parser,
            Some("gemma4")
                | Some("gemma-4")
                | Some("harmony")
                | Some("kimi_k2")
                | Some("kimi_k3")
                | Some("kimi-k3")
                | Some("minimax_m3")
                | Some("minimax-m3")
                | Some("minimax_m3_nom")
                | Some("minimax-m3-nom")
                | Some("inkling")
                | Some("muse_glimmer")
                | Some("muse")
                | Some("deepseek_v41")
        ) || matches!(
            reasoning_parser,
            Some("gemma4")
                | Some("gemma-4")
                | Some("gpt_oss")
                | Some("kimi_k25")
                | Some("kimi_k3")
                | Some("kimi-k3")
                | Some("mistral")
                | Some("minimax_m3")
                | Some("minimax-m3")
                | Some("inkling")
                | Some("muse_glimmer")
                | Some("muse")
                | Some("deepseek_v41")
        ))
    }

    /// Whether the resolved `skip_special_tokens` will strip the special-token
    /// markers an active parser depends on — guaranteeing the parser silently
    /// emits empty tool_calls / reasoning_content and the markup leaks into
    /// `content`. Only true when the caller explicitly forced
    /// `skip_special_tokens=true` while a special-token-dependent parser is
    /// active; otherwise the default is flipped to false before this check.
    fn special_tokens_will_be_stripped(
        skip_special_tokens: Option<bool>,
        parser_requires_special_tokens: bool,
    ) -> bool {
        skip_special_tokens == Some(true) && parser_requires_special_tokens
    }

    fn is_nemotron_force_reasoning(reasoning_parser: Option<&str>) -> bool {
        matches!(
            reasoning_parser,
            Some("nemotron_nano" | "nemotron3" | "nemotron_v3")
        )
    }

    /// Whether a request should surface parsed `reasoning_content` as `content`
    /// when no content was generated: any request carrying
    /// `force_nonempty_content=true`.
    ///
    /// Deliberately NOT keyed on the model or its reasoning parser. The flag is a
    /// request-level contract — "this response will have non-empty content" — so
    /// it is honored generically after parsing and before the response is sent,
    /// rather than being reimplemented inside each model-specific parser. Keying
    /// it on a parser allow-list means every new alias silently loses the
    /// behavior until someone edits the list.
    ///
    /// Note it rides in on `chat_template_args` but is NOT consumed by the chat
    /// template: the Nemotron template never reads `force_nonempty_content`, and
    /// rendering with it set produces a byte-identical prompt. It is a
    /// serving-layer flag that upstream happens to transport through the template
    /// kwargs, which is why the check belongs in postprocessing and not in a
    /// parser. So a client can set it on any model, and doing so is an explicit
    /// request for non-empty content — honoring it generically is the intent, not
    /// an accident of where the flag is declared.
    ///
    /// Drives both paths. The chat and Anthropic HTTP handlers pass it to the
    /// aggregator via `ParsingOptions::move_reasoning_to_content_when_empty` for
    /// non-streaming, and `postprocessor_parsing_stream` passes it as
    /// `defer_reasoning_for_nonempty_content` so the streaming path can hold
    /// reasoning back and reach the same answer at the terminal chunk. Both must
    /// use this one predicate or the two paths would disagree on the same input.
    ///
    /// `enable_thinking` is intentionally not consulted here: when thinking is
    /// off, reasoning parsing is disabled, so `reasoning_content` is always empty
    /// and the move is vacuous rather than suppressed.
    pub(crate) fn wants_reasoning_as_content_when_empty(
        chat_template_args: Option<&std::collections::HashMap<String, serde_json::Value>>,
    ) -> bool {
        chat_template_args.is_some_and(|args| {
            args.get("force_nonempty_content") == Some(&serde_json::Value::Bool(true))
        })
    }

    /// Whether this request's stream can withhold every data frame while it
    /// buffers, which is the only reason to force SSE keep-alive frames on.
    ///
    /// The HTTP handlers used to gate the heartbeat on
    /// `wants_reasoning_as_content_when_empty && reasoning_parser.is_some()`,
    /// but that is broader than the deferral it describes: the buffering only
    /// runs when `postprocessor_parsing_stream` takes its `should_parse_reasoning`
    /// branch. A `force_nonempty_content=true` request with reasoning disabled
    /// (`enable_thinking=false`) defers nothing, yet still got heartbeats — and
    /// the configured interval is opt-in precisely because some
    /// OpenAI-compatible clients do not ignore SSE comment frames.
    ///
    /// Known gap: `skip_reasoning_for_guided_json` also suppresses the deferral,
    /// but it is derived from guided-output inspection that is not available at
    /// the HTTP layer, so a guided-JSON bypass still enables the heartbeat when
    /// nothing is withheld. That direction is conservative — extra comment
    /// frames on an opt-in path rather than a silent stream — and closing it
    /// needs the guided-output derivation lifted out of the stream builder.
    pub(crate) fn stream_can_defer_all_output(
        tool_call_parser: Option<&str>,
        reasoning_parser: Option<&str>,
        chat_template_args: Option<&std::collections::HashMap<String, serde_json::Value>>,
    ) -> bool {
        // Unified families share reasoning-only output deferral with the legacy path.
        let has_reasoning_decoder = reasoning_parser.is_some()
            || crate::protocols::openai::chat_completions::unified_parser::selected_content_decoder_family(
                tool_call_parser,
                reasoning_parser,
            )
            .is_some();
        has_reasoning_decoder
            && Self::wants_reasoning_as_content_when_empty(chat_template_args)
            && !Self::is_reasoning_disabled_by_request(reasoning_parser, chat_template_args)
    }

    /// Parsers that begin streaming in reasoning mode (force_reasoning=true).
    /// These swallow any leading text without an open `<think>` tag as
    /// reasoning_content, so they cannot run on guided-decoding output where
    /// the model emits bare JSON from token 0.
    fn is_force_reasoning_parser(reasoning_parser: Option<&str>) -> bool {
        matches!(
            reasoning_parser,
            Some(
                "deepseek_r1"
                    | "deepseek_v3"
                    | "deepseek_v3_1"
                    | "deepseek_v3_2"
                    | "step3"
                    | "kimi_k25"
                    | "mistral"
                    | "minimax_m2"
                    | "minimax_append_think"
                    | "nemotron_nano"
                    | "nemotron3"
                    | "nemotron_v3"
            )
        )
    }

    /// Force-reasoning parsers proven to receive both bare guided JSON and
    /// native-reasoner-gated `reasoning</think>JSON`. These use the stream-shape
    /// detector instead of the historical unconditional guided-JSON bypass.
    fn supports_reasoning_before_guided_json(reasoning_parser: Option<&str>) -> bool {
        matches!(
            reasoning_parser,
            Some(
                "deepseek_r1"
                    | "deepseek_v3"
                    | "deepseek_v3_1"
                    | "deepseek_v3_2"
                    | "step3"
                    | "kimi_k25"
                    | "mistral"
                    | "minimax_m2"
                    | "nemotron_nano"
                    | "nemotron3"
                    | "nemotron_v3"
            )
        )
    }

    /// Reasoning openers that overlap with a valid guided-JSON prefix.
    fn guided_json_reasoning_start_token(reasoning_parser: Option<&str>) -> Option<&'static str> {
        match reasoning_parser {
            Some("mistral") => Some("[THINK]"),
            _ => None,
        }
    }

    fn skips_guided_json_when_prompt_injected(reasoning_parser: Option<&str>) -> bool {
        matches!(
            reasoning_parser,
            Some(
                "deepseek_v4"
                    | "deepseek_v41"
                    | "deepseek-v4"
                    | "deepseekv4"
                    | "glm45"
                    | "minimax_m3"
                    | "minimax-m3"
            )
        )
    }

    fn skips_structured_response_when_prompt_injected(reasoning_parser: Option<&str>) -> bool {
        matches!(reasoning_parser, Some("qwen3" | "kimi_k3" | "kimi-k3"))
            || Self::skips_guided_json_when_prompt_injected(reasoning_parser)
    }

    #[cfg(test)]
    fn configured_prompt_prefill(&self, formatted_prompt: Option<&str>) -> PromptReasoningPrefill {
        Self::prompt_reasoning_prefill_for_parsers(
            self.tool_call_parser.as_deref(),
            self.runtime_config.reasoning_parser.as_deref(),
            formatted_prompt,
        )
    }

    fn prompt_injected_reasoning_start(
        reasoning_parser: Option<&str>,
        formatted_prompt: Option<&str>,
    ) -> bool {
        let Some(prompt) = formatted_prompt.map(str::trim_end) else {
            return false;
        };

        match reasoning_parser {
            Some("minimax_m3") | Some("minimax-m3") => prompt.ends_with("<mm:think>"),
            Some("kimi_k3") | Some("kimi-k3") => {
                let mut suffix = prompt.chars().rev().filter(|c| !c.is_whitespace());
                "<|open|>think<|sep|>"
                    .chars()
                    .rev()
                    .all(|c| suffix.next() == Some(c))
            }
            Some("gemma4") => {
                prompt.ends_with("<|channel>thought") || prompt.ends_with("<|channel>")
            }
            Some("muse" | "muse_glimmer") => prompt.ends_with("assistant to=self<|message|>"),
            _ => prompt.ends_with("<think>"),
        }
    }

    fn prompt_injected_reasoning_start_for_parsers(
        tool_call_parser: Option<&str>,
        reasoning_parser: Option<&str>,
        formatted_prompt: Option<&str>,
    ) -> bool {
        let parser = crate::protocols::openai::chat_completions::unified_parser::configured_family(
            tool_call_parser,
            reasoning_parser,
        )
        .or(reasoning_parser);
        Self::prompt_injected_reasoning_start(parser, formatted_prompt)
    }

    fn prompt_reasoning_prefill_for_parsers(
        tool_call_parser: Option<&str>,
        reasoning_parser: Option<&str>,
        formatted_prompt: Option<&str>,
    ) -> PromptReasoningPrefill {
        let unified = Self::prompt_injected_reasoning_start_for_parsers(
            tool_call_parser,
            reasoning_parser,
            formatted_prompt,
        );
        let legacy = if matches!(reasoning_parser, Some("muse" | "muse_glimmer")) {
            // The legacy crate falls back to Basic for Muse, so only its generic
            // opener may seed reasoning. Native Muse prefill would swallow tool JSON.
            Self::prompt_injected_reasoning_start(None, formatted_prompt)
        } else {
            unified
        };
        let prefix =
            if crate::protocols::openai::chat_completions::unified_parser::configured_family(
                tool_call_parser,
                reasoning_parser,
            ) == Some("gemma4")
            {
                const HEADER: &str = "<|channel>thought\n";
                formatted_prompt.and_then(|raw| {
                    ("<|channel>".len()..HEADER.len())
                        .rev()
                        .map(|end| &HEADER[..end])
                        .find(|prefix| raw.ends_with(prefix))
                })
            } else {
                None
            };
        PromptReasoningPrefill {
            legacy,
            unified: unified || prefix.is_some(),
            prefix,
        }
    }

    fn prompt_injected_reasoning_ended_arg(
        reasoning_parser: Option<&str>,
        formatted_prompt: Option<&str>,
    ) -> Option<bool> {
        let should_forward = matches!(
            reasoning_parser,
            Some(
                "minimax_m2" | "minimax_m3" | "minimax-m3" | "kimi_k3" | "kimi-k3" | "deepseek_v41"
            )
        );
        if should_forward
            && Self::prompt_injected_reasoning_start(reasoning_parser, formatted_prompt)
        {
            Some(false)
        } else {
            None
        }
    }

    /// Check if reasoning parsing should be disabled based on per-request parameters.
    /// For kimi_k25/K3: disabled when chat_template_args contains "thinking": false.
    /// For Nemotron force-reasoning aliases: disabled when chat_template_args
    ///   contains "enable_thinking": false. "force_nonempty_content": true does
    ///   NOT disable parsing (streaming or non-streaming): the parser stays on so
    ///   reasoning is split from the answer, and reasoning is surfaced as content
    ///   only when no content was generated (non-streaming, in the aggregator).
    /// For DeepSeek: follows the same effective mode used by the prompt renderer.
    /// For Mistral: disabled unless `reasoning_effort` is present and not `none`.
    /// For Gemma 4: reasoning is opt-in and disabled unless chat_template_args
    ///   explicitly enables thinking. Gemma 4's chat template injects reasoning
    ///   markers only when enable_thinking=true.
    /// For MiniMax M3: disabled when chat_template_args contains
    ///   "thinking_mode": "disabled", matching SGLang's MiniMax M3 request
    ///   convention.
    pub(crate) fn request_disables_reasoning_with_family(
        request: &NvCreateChatCompletionRequest,
        tool_parser: Option<&str>,
        reasoning_parser: Option<&str>,
        default_thinking_mode: Option<&str>,
        unified_family: Option<&str>,
    ) -> bool {
        let mut chat_template_args = request.chat_template_args.clone();
        let from_client = Self::request_has_client_thinking_control(request);
        let constrained_generation = !from_client
            && Self::is_minimax_m3_family(reasoning_parser, tool_parser)
            && Self::has_minimax_m3_constrained_generation(request);
        Self::apply_default_thinking_mode_value(
            default_thinking_mode,
            &mut chat_template_args,
            from_client,
        );
        Self::normalize_thinking_args_with_source(
            &mut chat_template_args,
            reasoning_parser,
            tool_parser,
            from_client,
            constrained_generation,
        );
        // Unified owns both channels even when only a tool selector was configured.
        // The legacy split path retains its existing request-control behavior.
        if let Some(family) = unified_family {
            let thinking = dynamo_renderer::thinking_bool_from_args(chat_template_args.as_ref());
            return match family {
                "gemma4" => thinking != Some(true),
                "deepseek_v4" | "deepseek_v41" => {
                    !Self::deepseek_renderer_reasoning_enabled(chat_template_args.as_ref(), true)
                }
                _ => thinking == Some(false),
            };
        }
        Self::is_reasoning_disabled_by_request(reasoning_parser, chat_template_args.as_ref())
    }

    fn is_reasoning_disabled_by_request(
        reasoning_parser: Option<&str>,
        chat_template_args: Option<&std::collections::HashMap<String, serde_json::Value>>,
    ) -> bool {
        match reasoning_parser {
            Some("kimi_k25" | "kimi_k3" | "kimi-k3") => {
                dynamo_renderer::thinking_bool_from_args(chat_template_args) == Some(false)
            }
            parser if Self::is_nemotron_force_reasoning(parser) => {
                // `enable_thinking=false` turns reasoning off entirely (streaming
                // and non-streaming). `force_nonempty_content=true` does NOT
                // disable parsing: keeping the parser on splits reasoning from the
                // answer, so a reasoning+answer turn no longer leaks the reasoning
                // text and `</think>` into `content`.
                //
                // The reasoning-*only* move (surface reasoning as content when the
                // answer is empty) happens on both paths, by different means.
                // Non-streaming uses the aggregator flag
                // ParsingOptions::move_reasoning_to_content_when_empty. Streaming
                // cannot retract a reasoning_content delta already sent, so it
                // instead holds reasoning back until it knows whether an answer
                // follows — see `defer_reasoning_for_nonempty_content` and
                // `drain_deferred_reasoning`. Both end with the same contract: a
                // reasoning-only turn surfaces its text as `content`.
                dynamo_renderer::thinking_bool_from_args(chat_template_args) == Some(false)
            }
            Some("deepseek_v3" | "deepseek_v3_1") => {
                !Self::deepseek_renderer_reasoning_enabled(chat_template_args, false)
            }
            Some(
                "deepseek_r1" | "deepseek_v3_2" | "deepseek_v4" | "deepseek-v4" | "deepseekv4"
                | "deepseek_v41" | "minimax_m2",
            ) => !Self::deepseek_renderer_reasoning_enabled(chat_template_args, true),
            Some("gemma4") | Some("gemma-4") => {
                dynamo_renderer::thinking_bool_from_args(chat_template_args) != Some(true)
            }
            Some("mistral") => !Self::mistral_reasoning_enabled(chat_template_args),
            Some("minimax_m3") | Some("minimax-m3") => {
                if let Some(args) = chat_template_args
                    && let Some(mode) = args.get("thinking_mode").and_then(|v| v.as_str())
                {
                    return mode == "disabled";
                }
                false
            }
            _ => false,
        }
    }

    // Motivation: Each transformation on the stream should be a separate step to allow for more flexibility
    // Earlier reasoning parser logic was nested under delta generation logic in choice_from_postprocessor
    // Since we have tool calling parsing as separate step, it makes sense to have reasoning parser as separate step as well
    /// Apply reasoning parsing to the output stream, splitting content into
    /// `reasoning_content` and normal `content` based on think tags.
    ///
    /// When `prompt_injected_reasoning` is `true`, the parser starts in reasoning
    /// mode immediately — use this when the chat template already appended the
    /// reasoning start token (e.g., `<think>`) to the prompt, so the model's
    /// completion begins with thinking content without an explicit start tag.
    pub fn parse_reasoning_content_from_stream<S>(
        stream: S,
        parser_name: String,
        prompt_injected_reasoning: bool,
    ) -> impl Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send
    where
        S: Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send + 'static,
    {
        Self::parse_reasoning_content_from_stream_inner(
            stream,
            parser_name,
            prompt_injected_reasoning,
            false,
            false,
        )
    }

    fn parse_reasoning_content_from_stream_inner<S>(
        stream: S,
        parser_name: String,
        prompt_injected_reasoning: bool,
        bypass_bare_guided_json: bool,
        defer_reasoning_for_nonempty_content: bool,
    ) -> impl Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send
    where
        S: Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send + 'static,
    {
        // Parsers and bypass decisions are created lazily per `choice.index`
        // inside the unfold loop, so `n > 1` choices never share state.
        let state = ReasoningState {
            stream: Box::pin(stream),
            parser_name,
            prompt_injected_reasoning,
            bypass_bare_guided_json,
            choices: HashMap::new(),
            last_response: None,
            defer_reasoning_for_nonempty_content,
            saw_terminal_error: false,
        };

        stream::unfold(state, |mut state| async move {
            if let Some(response) = state.stream.next().await {
                // An error is terminal for the flush: latch it so the
                // end-of-stream branch below stays quiet. The chunk still takes
                // the normal path — `is_error()` keys on the annotation event,
                // not on `data`, so short-circuiting here would change how a
                // data-carrying error chunk is processed.
                if response.is_error() {
                    state.saw_terminal_error = true;
                }
                // Split disjoint field borrows so the per-choice map and the
                // parser-factory inputs can be used together inside map_data.
                // Scoped in a block so the borrows end before `state` moves.
                let processed_response = {
                    let ReasoningState {
                        parser_name,
                        prompt_injected_reasoning,
                        bypass_bare_guided_json,
                        choices,
                        defer_reasoning_for_nonempty_content,
                        ..
                    } = &mut state;
                    let parser_name = &*parser_name;
                    let prompt_injected_reasoning = *prompt_injected_reasoning;
                    let bypass_bare_guided_json = *bypass_bare_guided_json;
                    let defer_reasoning = *defer_reasoning_for_nonempty_content;

                    response.map_data(|mut data| {
                        for choice in data.inner.choices.iter_mut() {
                            let choice_state = choices.entry(choice.index).or_insert_with(|| {
                                let mut parser =
                                    Box::new(ReasoningParserType::get_reasoning_parser_from_name(
                                        parser_name,
                                    ))
                                        as Box<dyn ReasoningParser>;
                                if prompt_injected_reasoning {
                                    parser.set_in_reasoning(true);
                                }
                                ChoiceReasoningState {
                                    parser,
                                    guided_json_bypass_decision: (!bypass_bare_guided_json)
                                        .then_some(false),
                                    pending_reasoning: String::new(),
                                    pending_content: String::new(),
                                    left_reasoning: false,
                                    drained: false,
                                    parser_finished: false,
                                }
                            });

                            // Decide once per choice, from ITS OWN first
                            // non-whitespace content, whether the backend
                            // emitted bare guided JSON (`[`/`{`) that must reach
                            // the tool jail unparsed.
                            let bypass_decision = if bypass_bare_guided_json {
                                match choice_state.guided_json_bypass_decision {
                                    Some(decision) => Some(decision),
                                    None => {
                                        let decision = match choice.delta.content.as_ref() {
                                            Some(ChatCompletionMessageContent::Text(text)) => {
                                                let text = text.trim_start();
                                                if text.is_empty() {
                                                    None
                                                } else {
                                                    Some(matches!(text.as_bytes()[0], b'[' | b'{'))
                                                }
                                            }
                                            _ => None,
                                        };
                                        if let Some(decision) = decision {
                                            choice_state.guided_json_bypass_decision =
                                                Some(decision);
                                        }
                                        decision
                                    }
                                }
                            } else {
                                Some(false)
                            };

                            // Only a choice decided NOT to bypass is parsed. A
                            // bare-JSON or still-undecided (whitespace-only)
                            // choice keeps its content untouched for the jail.
                            // Reasoning parsing only applies to text content;
                            // multimodal content passes through unchanged.
                            if bypass_decision == Some(false)
                                && let Some(ChatCompletionMessageContent::Text(text)) =
                                    choice.delta.content.as_ref()
                            {
                                let parser_result = choice_state
                                    .parser
                                    .parse_reasoning_streaming_incremental(text, &[]);

                                // A backend that keeps sending content after this
                                // choice's `finish_reason` is out of protocol, but
                                // it has still fed bytes to the parser. Reopen the
                                // drain so they have somewhere to go at EOF instead
                                // of being stranded in parser state.
                                choice_state.drained = false;

                                if defer_reasoning && !choice_state.left_reasoning {
                                    // Still ambiguous: this text may be reasoning
                                    // followed by an answer, or the answer itself
                                    // reported as reasoning because the parser
                                    // starts inside the reasoning block. Hold it.
                                    choice_state
                                        .pending_reasoning
                                        .push_str(&parser_result.reasoning_text);
                                    // Whitespace-only normal text does not settle
                                    // anything: the aggregator treats such content
                                    // as empty (matching vLLM's
                                    // `not final_content.strip()`), so releasing
                                    // here would make streaming and non-streaming
                                    // disagree on the same turn. Hold it with the
                                    // reasoning until real answer text arrives.
                                    if parser_result.normal_text.trim().is_empty() {
                                        choice_state
                                            .pending_content
                                            .push_str(&parser_result.normal_text);
                                        choice.delta.content = None;
                                        choice.delta.reasoning_content = None;
                                    } else {
                                        // Real answer text means the parser left
                                        // the reasoning block, so everything held
                                        // so far really was reasoning.
                                        choice_state.left_reasoning = true;
                                        choice.delta.reasoning_content = (!choice_state
                                            .pending_reasoning
                                            .is_empty())
                                        .then(|| {
                                            std::mem::take(&mut choice_state.pending_reasoning)
                                        });
                                        let mut text =
                                            std::mem::take(&mut choice_state.pending_content);
                                        text.push_str(&parser_result.normal_text);
                                        choice.delta.content =
                                            Some(ChatCompletionMessageContent::Text(text));
                                    }
                                } else {
                                    choice.delta.content = parser_result
                                        .get_some_normal_text()
                                        .map(ChatCompletionMessageContent::Text);
                                    choice.delta.reasoning_content =
                                        parser_result.get_some_reasoning();
                                }
                            }

                            // This choice is finishing, so drain what it holds
                            // onto THIS delta. Emitting it as a later chunk
                            // would put content after `finish_reason` (and
                            // after the trailing usage chunk), where a client
                            // that stops at the terminal chunk never sees it.
                            // A parts delta has no text slot to append to, so
                            // draining onto it would leave the recovered text
                            // nowhere to go. Leave the choice undrained and let
                            // the end-of-stream fallback emit it as its own
                            // chunk instead of dropping it.
                            let terminal_carries_parts = matches!(
                                choice.delta.content,
                                Some(ChatCompletionMessageContent::Parts(_))
                            );
                            // Also require that this choice was actually parsed.
                            // A guided-JSON choice that bypassed the parser never
                            // fed it anything, so finishing that parser could only
                            // contribute text the choice never generated.
                            if defer_reasoning
                                && choice.finish_reason.is_some()
                                && !terminal_carries_parts
                                && bypass_decision == Some(false)
                            {
                                let (content, reasoning) = drain_deferred_reasoning(choice_state);
                                if let Some(content) = content {
                                    let merged = match choice.delta.content.take() {
                                        Some(ChatCompletionMessageContent::Text(existing)) => {
                                            existing + &content
                                        }
                                        Some(other) => {
                                            // Unreachable: guarded above.
                                            choice.delta.content = Some(other);
                                            content
                                        }
                                        None => content,
                                    };
                                    if choice.delta.content.is_none() {
                                        choice.delta.content =
                                            Some(ChatCompletionMessageContent::Text(merged));
                                    }
                                }
                                if let Some(reasoning) = reasoning {
                                    choice.delta.reasoning_content = Some(
                                        choice.delta.reasoning_content.take().unwrap_or_default()
                                            + &reasoning,
                                    );
                                }
                            }
                        }
                        Ok(data)
                    })
                };

                // Retain a spare envelope only when an EOF flush may follow, so
                // the common reasoning path avoids a full per-token clone. Skip
                // chunks with no choices (the trailing usage-only chunk): they
                // carry no delta slot, so using one as the flush envelope would
                // drop the very bytes the flush exists to preserve.
                if state.defer_reasoning_for_nonempty_content
                    && processed_response
                        .data
                        .as_ref()
                        .is_some_and(|data| !data.inner.choices.is_empty())
                {
                    state.last_response = Some(processed_response.clone());
                }
                Some((processed_response, state))
            } else if !state.defer_reasoning_for_nonempty_content || state.saw_terminal_error {
                // After a backend error the buffered bytes are dropped rather
                // than surfaced: the request failed, so there is no answer to
                // complete. See `saw_terminal_error`.
                None
            } else {
                // Upstream ended with no answer for the choices still holding a
                // pending buffer. Normally the terminal chunk already drained
                // them, so this is the fallback for a stream that ends without
                // any `finish_reason` — an aborted or truncated generation.
                // The synthetic chunk it builds is the only case where recovered
                // bytes arrive after the last upstream chunk, which is
                // unavoidable when there was no terminal chunk to attach them
                // to. Only the force_nonempty_content path reaches this branch;
                // every other parser took the `None` branch above and keeps its
                // original no-flush EOF behavior. Taking the envelope below
                // rather than cloning it makes this branch one-shot: once it is
                // gone the next poll ends the stream.
                // Sorted so the emitted choice order is deterministic rather
                // than following HashMap iteration order.
                let mut indices: Vec<u32> = state.choices.keys().copied().collect();
                indices.sort_unstable();
                #[allow(clippy::type_complexity)]
                let flushed: Vec<(u32, Option<String>, Option<String>)> = indices
                    .into_iter()
                    .filter_map(|index| {
                        let choice_state = state.choices.get_mut(&index)?;
                        if choice_state.guided_json_bypass_decision != Some(false) {
                            return None;
                        }
                        let (content, reasoning) = drain_deferred_reasoning(choice_state);
                        (content.is_some() || reasoning.is_some())
                            .then_some((index, content, reasoning))
                    })
                    .collect();
                if flushed.is_empty() {
                    return None;
                }
                let mut response = state.last_response.take()?;
                // See `scrub_synthetic_chunk_metadata`: this chunk produced no
                // tokens, so every per-chunk field from the envelope it was
                // cloned from has to be dropped rather than reported twice.
                scrub_synthetic_chunk_metadata(&mut response);
                let data = response.data.as_mut()?;
                // Rebuild the choice list from the flushed indices rather than
                // reusing the envelope's own choices: with `n > 1` the last
                // content-bearing chunk carries only the choices that happened
                // to be interleaved into it, which need not be the set that has
                // buffered bytes.
                let template = data.inner.choices.first()?.clone();
                data.inner.choices = flushed
                    .into_iter()
                    .map(|(index, content, reasoning)| {
                        let mut choice = template.clone();
                        choice.index = index;
                        choice.delta.role = None;
                        choice.delta.tool_calls = None;
                        choice.delta.function_call = None;
                        choice.delta.refusal = None;
                        choice.finish_reason = None;
                        choice.logprobs = None;
                        choice.delta.content = content.map(ChatCompletionMessageContent::Text);
                        choice.delta.reasoning_content = reasoning;
                        choice
                    })
                    .collect();
                Some((response, state))
            }
        })
        .fuse()
    }

    /// Apply the request-level non-empty-content contract after a unified parser has
    /// already split reasoning, content, and tool calls.
    fn defer_unified_reasoning_for_nonempty_content<S>(
        stream_in: S,
    ) -> impl Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send
    where
        S: Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send + 'static,
    {
        async_stream::stream! {
            let mut states: HashMap<u32, DeferredUnifiedChoice> = HashMap::new();
            let mut last_response: Option<Annotated<NvCreateChatCompletionStreamResponse>> = None;
            tokio::pin!(stream_in);

            while let Some(mut response) = stream_in.next().await {
                if response.is_error() {
                    yield response;
                    return;
                }
                let Some(data) = response.data.as_mut() else {
                    yield response;
                    continue;
                };
                let mut prefix_choices = Vec::new();

                for choice in &mut data.inner.choices {
                    let state = states.entry(choice.index).or_default();
                    if choice.delta.content.is_some()
                        || choice.delta.reasoning_content.is_some()
                        || choice.delta.tool_calls.is_some()
                    {
                        state.drained = false;
                    }

                    if !state.saw_visible_output
                        && let Some(reasoning) = choice.delta.reasoning_content.take()
                    {
                        state.pending_reasoning.push_str(&reasoning);
                    }

                    let carries_parts = matches!(
                        choice.delta.content,
                        Some(ChatCompletionMessageContent::Parts(_))
                    );
                    match choice.delta.content.take() {
                        Some(ChatCompletionMessageContent::Text(text)) => {
                            if !state.saw_visible_output && text.trim().is_empty() {
                                state.pending_content.push_str(&text);
                            } else {
                                if !text.trim().is_empty() {
                                    state.saw_visible_output = true;
                                }
                                let mut content = std::mem::take(&mut state.pending_content);
                                content.push_str(&text);
                                choice.delta.content =
                                    Some(ChatCompletionMessageContent::Text(content));
                            }
                        }
                        Some(parts @ ChatCompletionMessageContent::Parts(_)) => {
                            choice.delta.content = Some(parts);
                        }
                        None => {}
                    }

                    let has_tool_calls = choice
                        .delta
                        .tool_calls
                        .as_ref()
                        .is_some_and(|calls| !calls.is_empty());
                    if carries_parts || has_tool_calls {
                        state.saw_visible_output = true;
                    }
                    if carries_parts {
                        state.pending_content.clear();
                    }

                    if state.saw_visible_output {
                        let (content, reasoning) = state.release_split();
                        if let Some(reasoning) = reasoning {
                            let mut prefix = choice.clone();
                            prefix.delta.role = None;
                            prefix.delta.content = None;
                            prefix.delta.tool_calls = None;
                            prefix.delta.function_call = None;
                            prefix.delta.refusal = None;
                            prefix.delta.reasoning_content = Some(reasoning);
                            prefix.finish_reason = None;
                            prefix.logprobs = None;
                            prefix_choices.push(prefix);
                        }
                        if let Some(content) = content {
                            match choice.delta.content.as_mut() {
                                Some(ChatCompletionMessageContent::Text(existing)) => {
                                    existing.insert_str(0, &content);
                                }
                                Some(ChatCompletionMessageContent::Parts(_)) | None => {
                                    let mut prefix = choice.clone();
                                    prefix.delta.role = None;
                                    prefix.delta.content =
                                        Some(ChatCompletionMessageContent::Text(content));
                                    prefix.delta.tool_calls = None;
                                    prefix.delta.function_call = None;
                                    prefix.delta.refusal = None;
                                    prefix.delta.reasoning_content = None;
                                    prefix.finish_reason = None;
                                    prefix.logprobs = None;
                                    prefix_choices.push(prefix);
                                }
                            }
                        }
                    }

                    if choice.finish_reason.is_some() {
                        let (content, reasoning) = state.drain();
                        if let Some(content) = content {
                            match choice.delta.content.as_mut() {
                                Some(ChatCompletionMessageContent::Text(existing)) => {
                                    existing.push_str(&content);
                                }
                                Some(ChatCompletionMessageContent::Parts(_)) => {}
                                None => {
                                    choice.delta.content =
                                        Some(ChatCompletionMessageContent::Text(content));
                                }
                            }
                        }
                        if let Some(reasoning) = reasoning {
                            choice
                                .delta
                                .reasoning_content
                                .get_or_insert_default()
                                .push_str(&reasoning);
                        }
                    }
                }

                if !data.inner.choices.is_empty() {
                    last_response = Some(response.clone());
                }
                for prefix_choice in prefix_choices {
                    let mut prefix_response = response.clone();
                    scrub_synthetic_chunk_metadata(&mut prefix_response);
                    if let Some(prefix_data) = prefix_response.data.as_mut() {
                        prefix_data.inner.choices = vec![prefix_choice];
                    }
                    prefix_response.id = None;
                    yield prefix_response;
                }
                yield response;
            }

            let mut indices: Vec<_> = states.keys().copied().collect();
            indices.sort_unstable();
            let flushed: Vec<_> = indices
                .into_iter()
                .filter_map(|index| {
                    let (content, reasoning) = states.get_mut(&index)?.drain();
                    (content.is_some() || reasoning.is_some())
                        .then_some((index, content, reasoning))
                })
                .collect();
            if !flushed.is_empty()
                && let Some(mut response) = last_response
                && scrub_synthetic_chunk_metadata(&mut response).is_some()
                && let Some(data) = response.data.as_mut()
                && let Some(template) = data.inner.choices.first().cloned()
            {
                data.inner.choices = flushed
                    .into_iter()
                    .map(|(index, content, reasoning)| {
                        let mut choice = template.clone();
                        choice.index = index;
                        choice.delta.role = None;
                        choice.delta.content = content.map(ChatCompletionMessageContent::Text);
                        choice.delta.tool_calls = None;
                        choice.delta.function_call = None;
                        choice.delta.refusal = None;
                        choice.delta.reasoning_content = reasoning;
                        choice.finish_reason = None;
                        choice.logprobs = None;
                        choice
                    })
                    .collect();
                yield response;
            }
        }
    }

    /// Hold the trailing usage-only chunk until every parser recovery chunk has
    /// been emitted. A truncated upstream stream can end without
    /// `finish_reason`, so reasoning recovery happens at EOF; forwarding usage
    /// immediately would put that recovered content after the chunk clients
    /// treat as the stream trailer. A transport error discards the pending
    /// trailer because the response did not complete successfully.
    fn hold_usage_until_stream_end<S>(
        stream: S,
    ) -> impl Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send
    where
        S: Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send + 'static,
    {
        async_stream::stream! {
            tokio::pin!(stream);
            let mut pending_usage = None;
            let mut transport_failed = false;
            while let Some(response) = stream.next().await {
                if response.error.is_some() {
                    transport_failed = true;
                    pending_usage = None;
                    yield response;
                    continue;
                }
                let is_usage_only = response.data.as_ref().is_some_and(|data| {
                    data.inner.choices.is_empty() && data.inner.usage.is_some()
                });
                if is_usage_only {
                    if let Some(previous) = pending_usage.replace(response) {
                        yield previous;
                    }
                } else {
                    yield response;
                }
            }
            if !transport_failed && let Some(usage) = pending_usage {
                yield usage;
            }
        }
    }

    // Motivation: when Nemotron reasoning is disabled by request flags, the
    // backend may still emit a leading <think>. Buffer the initial stream
    // bytes so split chunks like "<thi" + "nk>answer" are stripped cleanly.
    fn strip_leading_reasoning_start_from_stream<S>(
        stream: S,
        think_start_token: &'static str,
    ) -> impl Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send
    where
        S: Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send + 'static,
    {
        struct StripReasoningStartState {
            stream:
                Pin<Box<dyn Stream<Item = Annotated<NvCreateChatCompletionStreamResponse>> + Send>>,
            think_start_token: &'static str,
            choices: HashMap<u32, StripChoiceState>,
            last_response: Option<Annotated<NvCreateChatCompletionStreamResponse>>,
            eof_flushed: bool,
        }

        #[derive(Default)]
        struct StripChoiceState {
            buffer: String,
            decided: bool,
        }

        fn take_undecided_buffer(choice_state: &mut StripChoiceState) -> Option<String> {
            if choice_state.decided || choice_state.buffer.is_empty() {
                return None;
            }

            choice_state.decided = true;
            Some(std::mem::take(&mut choice_state.buffer))
        }

        fn drain_undecided_buffers(
            choices: &mut HashMap<u32, StripChoiceState>,
        ) -> HashMap<u32, String> {
            choices
                .iter_mut()
                .filter_map(|(index, choice_state)| {
                    take_undecided_buffer(choice_state).map(|buffer| (*index, buffer))
                })
                .collect()
        }

        let state = StripReasoningStartState {
            stream: Box::pin(stream),
            think_start_token,
            choices: HashMap::new(),
            last_response: None,
            eof_flushed: false,
        };

        stream::unfold(state, |mut state| async move {
            if let Some(mut response) = state.stream.next().await {
                let Some(mut data) = response.data.take() else {
                    return Some((response, state));
                };

                for choice in data.inner.choices.iter_mut() {
                    let choice_state = state.choices.entry(choice.index).or_default();
                    let text = match choice.delta.content.take() {
                        Some(ChatCompletionMessageContent::Text(text)) => text,
                        other => {
                            if let Some(buffer) = take_undecided_buffer(choice_state) {
                                choice.delta.content =
                                    Some(ChatCompletionMessageContent::Text(buffer));
                            } else {
                                choice.delta.content = other;
                            }
                            continue;
                        }
                    };

                    let output = if choice_state.decided {
                        text
                    } else {
                        choice_state.buffer.push_str(&text);
                        let trimmed = choice_state.buffer.trim_start();
                        if trimmed.is_empty()
                            || (state.think_start_token.starts_with(trimmed)
                                && trimmed.len() < state.think_start_token.len())
                        {
                            choice.delta.content = None;
                            continue;
                        }

                        choice_state.decided = true;
                        if let Some(remainder) = trimmed.strip_prefix(state.think_start_token) {
                            remainder.to_string()
                        } else {
                            choice_state.buffer.clone()
                        }
                    };

                    choice_state.buffer.clear();
                    choice.delta.content = if output.is_empty() {
                        None
                    } else {
                        Some(ChatCompletionMessageContent::Text(output))
                    };
                }

                response.data = Some(data);
                if response
                    .data
                    .as_ref()
                    .is_some_and(|data| !data.inner.choices.is_empty())
                {
                    state.last_response = Some(response.clone());
                }

                Some((response, state))
            } else if state.eof_flushed {
                None
            } else {
                state.eof_flushed = true;
                let flushed = drain_undecided_buffers(&mut state.choices);
                if flushed.is_empty() {
                    None
                } else {
                    let mut response = state.last_response.clone()?;
                    // Same envelope problem as the reasoning-stream flush: this
                    // is a clone of an already-counted chunk, so it goes through
                    // the shared scrub rather than repeating a partial copy of
                    // it here.
                    scrub_synthetic_chunk_metadata(&mut response);
                    let data = response.data.as_mut()?;
                    let mut template = data.inner.choices.first()?.clone();
                    template.delta.role = None;
                    template.delta.tool_calls = None;
                    template.delta.function_call = None;
                    template.delta.refusal = None;
                    template.delta.reasoning_content = None;
                    template.finish_reason = None;
                    template.logprobs = None;
                    let mut flushed: Vec<_> = flushed.into_iter().collect();
                    flushed.sort_unstable_by_key(|(index, _)| *index);
                    data.inner.choices = flushed
                        .into_iter()
                        .map(|(index, buffer)| {
                            let mut choice = template.clone();
                            choice.index = index;
                            choice.delta.content = Some(ChatCompletionMessageContent::Text(buffer));
                            choice
                        })
                        .collect();

                    if data.inner.choices.is_empty() {
                        None
                    } else {
                        Some((response, state))
                    }
                }
            }
        })
        .fuse()
    }
}

fn preprocessing_lifecycle(context: &PipelineContext<()>) -> LifecycleTrace {
    context
        .get_optional::<LifecycleTrace>(LIFECYCLE_TRACE_CONTEXT_KEY)
        .ok()
        .flatten()
        .map(|trace| trace.as_ref().clone())
        // Responses and other callers may share this preprocessor without
        // creating a frontend lifecycle root. Never invent an orphan capture.
        .unwrap_or_else(|| LifecycleTrace::new(false))
}

// for pals, we do not want to add the generation prompt to the formatted prompt
// we also need to know if the template support this add_generation_prompt bool
// any prompt template that does not support this should return an error
// oob - we should update any prompt template that does not support this to support it

#[async_trait]
impl
    Operator<
        SingleIn<NvCreateChatCompletionRequest>,
        ManyOut<Annotated<NvCreateChatCompletionStreamResponse>>,
        SingleIn<PreprocessedRequest>,
        ManyOut<Annotated<BackendOutput>>,
    > for OpenAIPreprocessor
{
    async fn generate(
        &self,
        request: SingleIn<NvCreateChatCompletionRequest>,
        next: Arc<
            dyn AsyncEngine<SingleIn<PreprocessedRequest>, ManyOut<Annotated<BackendOutput>>, Error>,
        >,
    ) -> Result<ManyOut<Annotated<NvCreateChatCompletionStreamResponse>>, Error> {
        // unpack the request
        let (mut request, context) = request.into_parts();
        let lifecycle = preprocessing_lifecycle(&context);
        let preprocessing = lifecycle.start(LifecycleStage::RequestPreprocessing);

        // Preserve original inbound streaming flag before any internal overrides
        let request_id = context.id().to_string();
        let original_stream_flag = request.inner.stream.unwrap_or(false);

        // Build request payload handle (None if request trace is disabled / not eligible).
        // The handle snapshots the pristine request and its arrival time here;
        // the single payload record is published once at stream completion
        // (or with an empty response on cancel/timeout), off the request path.
        let payload_http_headers = if crate::request_trace::payload::http_header_capture_active() {
            context
                .get_optional::<std::collections::BTreeMap<String, String>>(
                    crate::request_trace::payload::HTTP_HEADERS_CONTEXT_KEY,
                )
                .ok()
                .flatten()
        } else {
            None
        };
        let payload_handle = crate::request_trace::payload::create_handle(
            &request,
            &request_id,
            payload_http_headers,
        );

        // For non-streaming requests (stream=false), enable usage by default
        // This ensures compliance with OpenAI API spec where non-streaming responses
        // always include usage statistics
        request.enable_usage_for_nonstreaming(original_stream_flag);

        // Set stream=true for internal processing (after request payload capture)
        request.inner.stream = Some(true);
        // Apply the deployment default before parser-specific normalization so
        // it can override an implicit model default (for example Kimi K2.5),
        // while explicit request controls still take precedence.
        let thinking_control_from_client = Self::request_has_client_thinking_control(&request);
        self.apply_default_thinking_mode(&mut request);
        Self::normalize_thinking_arg_with_source(
            &mut request,
            self.runtime_config.reasoning_parser.as_deref(),
            self.tool_call_parser.as_deref(),
            thinking_control_from_client,
        );
        Self::normalize_kimi_k3_named_tool_choice(&mut request, self.tool_call_parser.as_deref());

        // create a response generator
        let response_generator = request.response_generator(context.id().to_string());
        let tracker = Some(response_generator.tracker());
        let preprocess_options = PreprocessRequestOptions {
            preserve_omitted_max_tokens: context
                .get::<bool>(PRESERVE_OMITTED_MAX_TOKENS_CONTEXT_KEY)
                .ok()
                .is_some_and(|flag| *flag),
        };

        // convert the chat completion request to a common completion request
        let (mut common_request, annotations, prompt_injected_reasoning, image_tokens) = self
            .preprocess_request_with_options(
                &request,
                tracker.as_deref(),
                preprocess_options,
                context
                    .get_optional::<String>(LORA_NAME_CONTEXT_KEY)
                    .ok()
                    .flatten()
                    .map(|name| name.as_ref().clone()),
            )
            .instrument(preprocessing.clone())
            .await?;
        attach_request_context_metadata(&mut common_request, &context);

        let guided_tool_constraint = self.apply_tool_choice_guided_decoding(
            &request,
            &mut common_request,
            prompt_injected_reasoning.unified,
        )?;
        let tool_processing_route =
            self.tool_processing_route(&request, &guided_tool_constraint)?;
        let payload_parsing_options = payload_handle.as_ref().map(|_| {
            context
                .get_optional::<ParsingOptions>(REQUEST_PARSING_OPTIONS_CONTEXT_KEY)
                .ok()
                .flatten()
                .map(|options| options.as_ref().clone())
                .unwrap_or_else(|| {
                    let mut options = ParsingOptions::new(
                        self.tool_call_parser.clone(),
                        self.runtime_config.reasoning_parser.clone(),
                    );
                    options.tool_choice = request.inner.tool_choice.clone();
                    options.guided_tool_constraint = guided_tool_constraint.clone();
                    options.parallel_tool_calls = request.inner.parallel_tool_calls;
                    options
                        .with_tool_call_parsing_enabled(Self::tool_call_parsing_enabled(&request))
                        .with_move_reasoning_to_content_when_empty(
                            Self::wants_reasoning_as_content_when_empty(
                                request.chat_template_args.as_ref(),
                            ),
                        )
                })
        });
        validate_legacy_jail_nvext_choice_count(
            request.inner.n.unwrap_or(1),
            request
                .nvext
                .as_ref()
                .and_then(|nvext| nvext.extra_fields.as_deref()),
            tool_processing_route.uses_legacy_jail(),
        )?;

        tracing::trace!(request = ?common_request, ?prompt_injected_reasoning, "Pre-processed request");
        let trace_state = crate::request_trace::build_request_end_trace_state(
            &common_request,
            &tracker,
            &context,
            self.kv_cache_block_size,
        );
        let trace_tokens_enabled = trace_state.is_some();
        let trace_finish_reason_metadata =
            crate::request_trace::finish_reason_metadata_handle(&trace_state);

        // Attach the timing tracker to the request so downstream components can record metrics
        common_request.tracker = tracker;

        // Capture media counts before `common_request` is moved into the context.
        let mm_counts = MultimodalCounts::from_preprocessed(&common_request);

        let mut response_generator = Box::new(response_generator);

        // Update ISL only for text prompts (embeddings get sequence length from tensor shape)
        if common_request.prompt_embeds.is_none() {
            let isl = common_request.token_ids.len() as u32;
            response_generator.update_isl(isl);
        }

        // repack the common completion request
        let common_request = context.map(|_| common_request);

        // create a stream of annotations this will be prepend to the response stream
        let annotations: Vec<Annotated<NvCreateChatCompletionStreamResponse>> = annotations
            .into_iter()
            .flat_map(|(k, v)| Annotated::from_annotation(k, &v))
            .collect();
        let annotations_stream = stream::iter(annotations);
        drop(preprocessing);

        // forward the common completion request to the next operator
        let response_stream = next.generate(common_request).await?;
        // Extract context once
        let context = response_stream.context();

        // transform the postprocessor stream (no boxing yet) - detokenize
        let stream = Self::transform_postprocessor_stream_with_image_tokens(
            response_stream,
            response_generator,
            context.clone(),
            payload_handle.is_some(),
            trace_tokens_enabled,
            trace_finish_reason_metadata,
            mm_counts,
            image_tokens,
        );

        let transformed_stream = self.postprocessor_parsing_stream_with_constraint(
            stream,
            &request,
            prompt_injected_reasoning,
            guided_tool_constraint,
            tool_processing_route,
        )?;
        let transformed_stream = Self::normalize_chat_stream_roles(transformed_stream);

        // Request payload capture is a pass-through: every chunk reaches the HTTP
        // layer unchanged (metrics, errors, aggregation all behave as with capture
        // off) while a copy is aggregated on the side for the record.
        let final_stream = if let Some(payload) = payload_handle {
            let parsing_options = payload_parsing_options
                .expect("payload capture has matching request parsing options");
            let (stream, agg_fut) =
                crate::request_trace::payload_stream::scan_aggregate_with_future(
                    Box::pin(transformed_stream),
                    parsing_options,
                );

            // Spawn the payload emit off the request path. The outcome carries a drop
            // reason and any recovered partial response, so emit the record either way.
            tokio::spawn(async move {
                let outcome = agg_fut.await;
                if let Some(reason) = outcome.drop_reason.as_deref() {
                    tracing::debug!(
                        request_id = %payload.request_id(),
                        drop_reason = %reason,
                        partial_response = outcome.response.is_some(),
                        "request payload: response aggregation incomplete; emitting record with drop reason"
                    );
                }
                payload.emit(outcome.response.map(Arc::new), outcome.drop_reason);
            });

            stream
        } else {
            Box::pin(transformed_stream)
        };

        // Step 5: Speculative next-turn prefill
        let final_stream = speculative_prefill::maybe_wrap_stream(
            final_stream,
            &request,
            &request_id,
            &next,
            &self.formatter,
            &self.tokenizer,
            &self.speculative_prefill_tasks,
        );

        let final_stream =
            crate::request_trace::wrap_chat_request_end_stream(final_stream, trace_state);

        // prepend the annotations to the response stream
        let stream = annotations_stream.chain(final_stream);

        // return the response stream - single boxing at the end
        Ok(ResponseStream::new(Box::pin(stream), context))
    }
}

#[async_trait]
impl
    Operator<
        SingleIn<NvCreateCompletionRequest>,
        ManyOut<Annotated<NvCreateCompletionResponse>>,
        SingleIn<PreprocessedRequest>,
        ManyOut<Annotated<BackendOutput>>,
    > for OpenAIPreprocessor
{
    async fn generate(
        &self,
        request: SingleIn<NvCreateCompletionRequest>,
        next: Arc<
            dyn AsyncEngine<SingleIn<PreprocessedRequest>, ManyOut<Annotated<BackendOutput>>, Error>,
        >,
    ) -> Result<ManyOut<Annotated<NvCreateCompletionResponse>>, Error> {
        let _stage_guard = StageGuard::new(STAGE_PREPROCESS, "");

        // unpack the request
        let (mut request, context) = request.into_parts();
        let request_id = context.id().to_string();

        // Preserve original streaming flag
        let original_stream_flag = request.inner.stream.unwrap_or(false);

        // For non-streaming requests (stream=false), enable usage by default
        // This ensures compliance with OpenAI API spec where non-streaming responses
        // always include usage statistics
        request.enable_usage_for_nonstreaming(original_stream_flag);

        request.inner.stream = Some(true);

        // create a response generator
        let response_generator = request.response_generator(request_id.clone());
        let mut response_generator = Box::new(response_generator);
        let tracker = Some(response_generator.tracker());
        // convert the chat completion request to a common completion request
        let mut builder = self.builder_with_lora(
            &request,
            context
                .get_optional::<String>(LORA_NAME_CONTEXT_KEY)
                .ok()
                .flatten()
                .map(|name| name.as_ref().clone()),
        )?;

        // Check if embeddings are provided - skip tokenization path
        let annotations = if let Some(ref prompt_embeds) = request.inner.prompt_embeds {
            // Skip tokenization for embeddings
            builder.token_ids(vec![]); // Empty token IDs
            builder.prompt_embeds(Some(prompt_embeds.clone()));
            // No token annotations
            HashMap::new()
        } else {
            // Normal path: tokenize the prompt; embeddings don't need MM routing,
            // so install tokens on the builder right away.
            let (token_ids, ann) = self
                .gather_tokens(&request, None, tracker.as_deref())
                .await?;
            builder.token_ids(token_ids);
            ann
        };

        // Gather multimodal data (works with both embeddings and text prompts)
        // Returned MM entries are unused on the embeddings path; routing info is
        // not built here.
        let _ = self
            .gather_multi_modal_data(&request, &mut builder, None, &[])
            .await?;

        let mut common_request = builder.build()?;
        Self::validate_preprocessed_token_budget(&common_request, self.token_budget.as_ref())?;
        attach_request_context_metadata(&mut common_request, &context);

        let trace_state = crate::request_trace::build_request_end_trace_state(
            &common_request,
            &tracker,
            &context,
            self.kv_cache_block_size,
        );
        let trace_tokens_enabled = trace_state.is_some();
        let trace_finish_reason_metadata =
            crate::request_trace::finish_reason_metadata_handle(&trace_state);

        // Attach the timing tracker to the request so downstream components can record metrics
        common_request.tracker = tracker;

        // Update ISL only for text prompts (embeddings get sequence length from tensor shape)
        if common_request.prompt_embeds.is_none() {
            let isl = common_request.token_ids.len() as u32;
            response_generator.update_isl(isl);
        }

        // repack the common completion request
        let common_request = context.map(|_| common_request);

        // create a stream of annotations this will be prepend to the response stream
        let annotations: Vec<Annotated<NvCreateCompletionResponse>> = annotations
            .into_iter()
            .flat_map(|(k, v)| Annotated::from_annotation(k, &v))
            .collect();
        let annotations_stream = stream::iter(annotations);

        // End preprocess stage before handing off to downstream (route/dispatch).
        drop(_stage_guard);

        // forward the common completion request to the next operator
        let response_stream = next.generate(common_request).await?;

        // Extract context once
        let context = response_stream.context();

        // transform the postprocessor stream. Legacy `/v1/completions` is
        // text-only, so multimodal counts are always zero here.
        let stream = Self::transform_postprocessor_stream(
            response_stream,
            response_generator,
            context.clone(),
            false,
            trace_tokens_enabled,
            trace_finish_reason_metadata,
            MultimodalCounts::default(),
        );

        let stream =
            crate::request_trace::wrap_completion_request_end_stream(Box::pin(stream), trace_state);

        // prepend the annotations to the response stream
        let stream = annotations_stream.chain(stream);

        // return the response stream
        Ok(ResponseStream::new(Box::pin(stream), context))
    }
}

#[async_trait]
impl
    Operator<
        SingleIn<NvCreateEmbeddingRequest>,
        ManyOut<Annotated<NvCreateEmbeddingResponse>>,
        SingleIn<PreprocessedEmbeddingRequest>,
        ManyOut<Annotated<EmbeddingsEngineOutput>>,
    > for OpenAIPreprocessor
{
    async fn generate(
        &self,
        request: SingleIn<NvCreateEmbeddingRequest>,
        next: Arc<
            dyn AsyncEngine<
                    SingleIn<PreprocessedEmbeddingRequest>,
                    ManyOut<Annotated<EmbeddingsEngineOutput>>,
                    Error,
                >,
        >,
    ) -> Result<ManyOut<Annotated<NvCreateEmbeddingResponse>>, Error> {
        // Unpack request
        let (request, context) = request.into_parts();

        // Preprocess the embedding request
        let (preprocessed_request, annotations) =
            self.preprocess_embedding_request(&request).await?;

        // Forward to next stage
        let preprocessed_request = context.map(|_| preprocessed_request);
        let response_stream = next.generate(preprocessed_request).await?;

        // Extract context once
        let context = response_stream.context();

        // Transform response stream back to OpenAI format
        let stream = Self::transform_embedding_postprocessor_stream(response_stream, request);

        // Prepend annotations
        let annotations_stream = stream::iter(
            annotations
                .into_iter()
                .flat_map(|(k, v)| Annotated::from_annotation(k, &v))
                .collect::<Vec<_>>(),
        );

        let combined_stream = annotations_stream.chain(stream);
        Ok(ResponseStream::new(Box::pin(combined_stream), context))
    }
}

// Note: tests for jailing and parser detection live in `lib/llm/tests/test_jail.rs`

#[cfg(test)]
mod token_data_tests {
    use super::*;
    use crate::common::checked_file::CheckedFile;
    use crate::model_card::{ModelDeploymentCard, ModelInfoType, TokenizerKind};

    /// `config.json` has `vocab_size` 128256; the mock tokenizer is smaller.
    const LLAMA_DIR: &str = "tests/data/sample-models/mock-llama-3.1-8b-instruct";
    /// The tokenizer has 32000 ids.
    const TINYLLAMA_DIR: &str = "tests/data/sample-models/TinyLlama_v1.1";
    const NO_VOCAB_SIZE: &str = r#"{"architectures":[],"model_type":"","eos_token_id":2}"#;
    /// Ids 0 and 2 only, as in the HF `incomplete_vocab` test.
    const GAP_TOKENIZER: &str = r#"{"version":"1.0","added_tokens":[],
        "model":{"type":"WordLevel","vocab":{"<unk>":0,"b":2},"unk_token":"<unk>"}}"#;
    /// Ids 0 and 1, and an added token that HF gives id 2.
    const ADDED_TOKEN_TOKENIZER: &str = r#"{"version":"1.0","added_tokens":[{"id":2,
        "content":"<x>","special":true,"single_word":false,"lstrip":false,"rstrip":false,
        "normalized":false}],
        "model":{"type":"WordLevel","vocab":{"<unk>":0,"a":1},"unk_token":"<unk>"}}"#;

    /// TinyLlama's card with the given `config.json` and, if set, `tokenizer.json`.
    fn tinyllama_card(
        config: &str,
        tokenizer: Option<&str>,
    ) -> (ModelDeploymentCard, tempfile::TempDir) {
        let mut mdc = ModelDeploymentCard::load_from_disk(TINYLLAMA_DIR, None).unwrap();
        let dir = tempfile::tempdir().unwrap();
        let path = dir.path().join("config.json");
        std::fs::write(&path, config).unwrap();
        mdc.model_info = Some(ModelInfoType::HfConfigJson(
            CheckedFile::from_disk(&path).unwrap(),
        ));
        if let Some(tokenizer) = tokenizer {
            let path = dir.path().join("tokenizer.json");
            std::fs::write(&path, tokenizer).unwrap();
            mdc.tokenizer = Some(TokenizerKind::HfTokenizerJson(
                CheckedFile::from_disk(&path).unwrap(),
            ));
        }
        (mdc, dir)
    }

    /// TinyLlama's tokenizer with the given `config.json`.
    fn tinyllama_with_config(config: &str) -> (Arc<OpenAIPreprocessor>, tempfile::TempDir) {
        let (mdc, dir) = tinyllama_card(config, None);
        (OpenAIPreprocessor::new(mdc).unwrap(), dir)
    }

    fn request(token_data: &[u32]) -> NvCreateChatCompletionRequest {
        serde_json::from_value(serde_json::json!({
            "model": "test-model",
            "messages": [{"role": "user", "content": "hi"}],
            "nvext": {"token_data": token_data}
        }))
        .unwrap()
    }

    async fn assert_rejected(preprocessor: &OpenAIPreprocessor, token_data: &[u32]) {
        let error = preprocessor
            .preprocess_request(&request(token_data), None)
            .await
            .unwrap_err();
        assert_eq!(
            error
                .downcast_ref::<DynamoError>()
                .map(DynamoError::error_type),
            Some(ErrorType::InvalidArgument),
            "{error:#}"
        );
    }

    async fn assert_forwarded(preprocessor: &OpenAIPreprocessor, token_data: &[u32]) {
        let (preprocessed, _, _) = preprocessor
            .preprocess_request(&request(token_data), None)
            .await
            .unwrap();
        assert_eq!(preprocessed.token_ids.as_slice(), token_data);
    }

    #[tokio::test]
    async fn token_data_is_bounded_by_model_vocab() {
        let mdc = ModelDeploymentCard::load_from_disk(LLAMA_DIR, None).unwrap();
        let preprocessor = OpenAIPreprocessor::new(mdc).unwrap();
        assert_rejected(&preprocessor, &[1, 128256]).await;
        assert_rejected(&preprocessor, &[u32::MAX]).await;
        assert_forwarded(&preprocessor, &[1, 128255]).await;
    }

    #[tokio::test]
    async fn token_data_is_bounded_by_tokenizer_vocab() {
        // No `vocab_size` in config.json: the tokenizer sets the bound.
        let (preprocessor, _dir) =
            tinyllama_with_config(r#"{"architectures":[],"model_type":"","eos_token_id":2}"#);
        assert_rejected(&preprocessor, &[1, 32000]).await;
        assert_forwarded(&preprocessor, &[1, 31999]).await;

        // A tokenizer id at or above the model's `vocab_size` stays valid.
        let (preprocessor, _dir) = tinyllama_with_config(
            r#"{"architectures":[],"model_type":"","eos_token_id":2,"vocab_size":31999}"#,
        );
        assert_forwarded(&preprocessor, &[1, 31999]).await;
        assert_rejected(&preprocessor, &[1, 32000]).await;
    }

    #[tokio::test]
    async fn token_data_is_bounded_by_largest_tokenizer_id() {
        let (mdc, _dir) = tinyllama_card(NO_VOCAB_SIZE, Some(GAP_TOKENIZER));
        let encoding = mdc.tokenizer().unwrap().encode("b").unwrap();
        assert_eq!(encoding.token_ids(), [2]);
        let preprocessor = OpenAIPreprocessor::new(mdc).unwrap();
        assert_forwarded(&preprocessor, &[0, 2]).await;
        assert_rejected(&preprocessor, &[0, 3]).await;
    }

    #[tokio::test]
    async fn token_data_is_bounded_by_added_tokens() {
        let (mdc, _dir) = tinyllama_card(NO_VOCAB_SIZE, Some(ADDED_TOKEN_TOKENIZER));
        let encoding = mdc.tokenizer().unwrap().encode("<x>").unwrap();
        assert_eq!(encoding.token_ids(), [2]);
        let preprocessor = OpenAIPreprocessor::new(mdc).unwrap();
        assert_forwarded(&preprocessor, &[0, 2]).await;
        assert_rejected(&preprocessor, &[0, 3]).await;
    }

    fn completion(prompt: serde_json::Value) -> NvCreateCompletionRequest {
        serde_json::from_value(serde_json::json!({"model": "test-model", "prompt": prompt}))
            .unwrap()
    }

    #[tokio::test]
    async fn token_prompt_is_bounded_by_model_vocab() {
        let mdc = ModelDeploymentCard::load_from_disk(LLAMA_DIR, None).unwrap();
        let preprocessor = OpenAIPreprocessor::new(mdc).unwrap();
        for prompt in [
            serde_json::json!([1, 128256]),
            serde_json::json!([[1, 128256]]),
            serde_json::json!([u32::MAX]),
        ] {
            let error = preprocessor
                .preprocess_request(&completion(prompt), None)
                .await
                .unwrap_err();
            assert_eq!(
                error
                    .downcast_ref::<DynamoError>()
                    .map(DynamoError::error_type),
                Some(ErrorType::InvalidArgument),
                "{error:#}"
            );
        }
        let in_range: [u32; 2] = [1, 128255];
        for prompt in [serde_json::json!(in_range), serde_json::json!([in_range])] {
            let (preprocessed, _, _) = preprocessor
                .preprocess_request(&completion(prompt), None)
                .await
                .unwrap();
            assert_eq!(preprocessed.token_ids.as_slice(), in_range);
        }
    }

    #[tokio::test]
    async fn token_prompt_is_bounded_by_largest_tokenizer_id() {
        let (mdc, _dir) = tinyllama_card(NO_VOCAB_SIZE, Some(GAP_TOKENIZER));
        let preprocessor = OpenAIPreprocessor::new(mdc).unwrap();
        let (preprocessed, _, _) = preprocessor
            .preprocess_request(&completion(serde_json::json!([0, 2])), None)
            .await
            .unwrap();
        assert_eq!(preprocessed.token_ids.as_slice(), [0, 2]);
        let error = preprocessor
            .preprocess_request(&completion(serde_json::json!([0, 3])), None)
            .await
            .unwrap_err();
        assert_eq!(
            error
                .downcast_ref::<DynamoError>()
                .map(DynamoError::error_type),
            Some(ErrorType::InvalidArgument),
            "{error:#}"
        );
    }

    /// Records the token ids of each request that reaches the backend.
    #[derive(Default)]
    struct RecordingEmbeddingBackend(Mutex<Vec<Vec<Vec<u32>>>>);

    #[async_trait]
    impl
        AsyncEngine<
            SingleIn<PreprocessedEmbeddingRequest>,
            ManyOut<Annotated<EmbeddingsEngineOutput>>,
            Error,
        > for RecordingEmbeddingBackend
    {
        async fn generate(
            &self,
            request: SingleIn<PreprocessedEmbeddingRequest>,
        ) -> Result<ManyOut<Annotated<EmbeddingsEngineOutput>>, Error> {
            let (request, context) = request.transfer(());
            self.0.lock().unwrap().push(request.token_ids);
            Ok(ResponseStream::new(
                Box::pin(stream::empty()),
                context.context(),
            ))
        }
    }

    #[tokio::test]
    async fn embedding_token_input_is_bounded_by_model_vocab() {
        let mut mdc = ModelDeploymentCard::load_from_disk(LLAMA_DIR, None).unwrap();
        mdc.model_type = crate::model_type::ModelType::Embedding;
        let preprocessor = OpenAIPreprocessor::new_for_embeddings(mdc).unwrap();
        let backend = Arc::new(RecordingEmbeddingBackend::default());
        let next: Arc<
            dyn AsyncEngine<
                    SingleIn<PreprocessedEmbeddingRequest>,
                    ManyOut<Annotated<EmbeddingsEngineOutput>>,
                    Error,
                >,
        > = backend.clone();
        let embed = |input: serde_json::Value| {
            let request: NvCreateEmbeddingRequest =
                serde_json::from_value(serde_json::json!({"model": "test-model", "input": input}))
                    .unwrap();
            PipelineContext::new(request)
        };

        for input in [
            serde_json::json!([1, 128256]),
            serde_json::json!([[1, 2], [1, 128256]]),
            serde_json::json!([u32::MAX]),
        ] {
            let Err(error) =
                Operator::generate(preprocessor.as_ref(), embed(input), next.clone()).await
            else {
                panic!("an out-of-range token input must fail");
            };
            assert_eq!(
                error
                    .downcast_ref::<DynamoError>()
                    .map(DynamoError::error_type),
                Some(ErrorType::InvalidArgument),
                "{error:#}"
            );
        }
        assert!(
            backend.0.lock().unwrap().is_empty(),
            "no request reached the backend"
        );

        for input in [
            serde_json::json!([1, 128255]),
            serde_json::json!([[1, 2], [3, 128255]]),
        ] {
            Operator::generate(preprocessor.as_ref(), embed(input), next.clone())
                .await
                .unwrap();
        }
        assert_eq!(
            *backend.0.lock().unwrap(),
            vec![vec![vec![1, 128255]], vec![vec![1, 2], vec![3, 128255]]]
        );
    }

    #[tokio::test]
    async fn embedding_token_input_is_bounded_by_largest_tokenizer_id() {
        let (mut mdc, _dir) = tinyllama_card(NO_VOCAB_SIZE, Some(GAP_TOKENIZER));
        mdc.model_type = crate::model_type::ModelType::Embedding;
        let preprocessor = OpenAIPreprocessor::new_for_embeddings(mdc).unwrap();
        let backend = Arc::new(RecordingEmbeddingBackend::default());
        let next: Arc<
            dyn AsyncEngine<
                    SingleIn<PreprocessedEmbeddingRequest>,
                    ManyOut<Annotated<EmbeddingsEngineOutput>>,
                    Error,
                >,
        > = backend.clone();
        let embed = |input: serde_json::Value| {
            let request: NvCreateEmbeddingRequest =
                serde_json::from_value(serde_json::json!({"model": "test-model", "input": input}))
                    .unwrap();
            PipelineContext::new(request)
        };

        Operator::generate(
            preprocessor.as_ref(),
            embed(serde_json::json!([0, 2])),
            next.clone(),
        )
        .await
        .unwrap();
        let Err(error) = Operator::generate(
            preprocessor.as_ref(),
            embed(serde_json::json!([0, 3])),
            next.clone(),
        )
        .await
        else {
            panic!("an out-of-range token input must fail");
        };
        assert_eq!(
            error
                .downcast_ref::<DynamoError>()
                .map(DynamoError::error_type),
            Some(ErrorType::InvalidArgument),
            "{error:#}"
        );
        assert_eq!(*backend.0.lock().unwrap(), vec![vec![vec![0, 2]]]);
    }

    #[test]
    fn token_id_bound_ignores_unknown_and_zero_sizes() {
        assert_eq!(token_id_bound(Some(151936), Some(151669)), Some(151936));
        assert_eq!(token_id_bound(Some(128256), Some(128257)), Some(128257));
        assert_eq!(token_id_bound(None, Some(32000)), Some(32000));
        assert_eq!(token_id_bound(Some(0), Some(0)), None);
        assert_eq!(token_id_bound(None, None), None);
        assert!(ensure_token_ids_in_vocab("prompt", &[u32::MAX], None).is_ok());
    }
}

#[cfg(test)]
mod strip_tests {
    use super::OpenAIPreprocessor;

    #[test]
    fn test_strip_inline_data_urls_replaces_data_urls() {
        let mut messages = serde_json::json!([{
            "role": "user",
            "content": [
                {"type": "text", "text": "What is this?"},
                {"type": "image_url", "image_url": {"url": "data:image/png;base64,iVBOR...longdata..."}},
                {"type": "image_url", "image_url": {"url": "https://example.com/img.png"}}
            ]
        }]);
        OpenAIPreprocessor::strip_inline_data_urls(&mut messages);
        let parts = messages[0]["content"].as_array().unwrap();
        assert_eq!(parts[0]["text"], "What is this?");
        assert_eq!(parts[1]["image_url"]["url"], "");
        assert_eq!(parts[2]["image_url"]["url"], "https://example.com/img.png");
    }

    #[test]
    fn test_strip_inline_data_urls_handles_video_audio() {
        let mut messages = serde_json::json!([{
            "role": "user",
            "content": [
                {"type": "video_url", "video_url": {"url": "data:video/mp4;base64,AAAA..."}},
                {"type": "audio_url", "audio_url": {"url": "https://example.com/audio.wav"}}
            ]
        }]);
        OpenAIPreprocessor::strip_inline_data_urls(&mut messages);
        let parts = messages[0]["content"].as_array().unwrap();
        assert_eq!(parts[0]["video_url"]["url"], "");
        assert_eq!(
            parts[1]["audio_url"]["url"],
            "https://example.com/audio.wav"
        );
    }

    #[test]
    fn test_strip_inline_data_urls_preserves_text_only() {
        let mut messages = serde_json::json!([{
            "role": "user",
            "content": "plain text message"
        }]);
        let original = messages.clone();
        OpenAIPreprocessor::strip_inline_data_urls(&mut messages);
        assert_eq!(messages, original);
    }

    #[test]
    fn test_strip_inline_data_urls_empty_messages() {
        let mut messages = serde_json::json!([]);
        OpenAIPreprocessor::strip_inline_data_urls(&mut messages);
        assert_eq!(messages, serde_json::json!([]));
    }

    #[test]
    fn test_strip_inline_data_urls_malformed_does_not_panic() {
        for mut messages in [
            serde_json::json!(null),
            serde_json::json!({"role": "user"}),
            serde_json::json!([{"role": "user"}]),
            serde_json::json!([{"role": "user", "content": {"text": "x"}}]),
            serde_json::json!([{
                "role": "user",
                "content": [
                    "plain",
                    {"type": "image_url"},
                    {"type": "image_url", "image_url": "https://example.com/x.png"},
                    {"type": "image_url", "image_url": {"url": 1}},
                    {"type": "image_url", "image_url": {"url": "data:image/png;base64,QQ"}}
                ]
            }]),
        ] {
            OpenAIPreprocessor::strip_inline_data_urls(&mut messages);
        }
        let mut two_inline = serde_json::json!([{
            "role": "user",
            "content": [
                {"type": "image_url", "image_url": {"url": "data:image/png;base64,AAA"}},
                {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,BBB"}}
            ]
        }]);
        OpenAIPreprocessor::strip_inline_data_urls(&mut two_inline);
        assert_eq!(two_inline[0]["content"][0]["image_url"]["url"], "");
        assert_eq!(two_inline[0]["content"][1]["image_url"]["url"], "");
    }
}

#[cfg(test)]
mod extra_args_media_copy_tests {
    use super::*;
    use crate::model_card::ModelDeploymentCard;
    use crate::protocols::common::preprocessor::MultimodalData;

    fn test_preprocessor() -> OpenAIPreprocessor {
        let mdc = ModelDeploymentCard::load_from_disk(
            "tests/data/sample-models/mock-llama-3.1-8b-instruct",
            None,
        )
        .unwrap();
        let preprocessor = OpenAIPreprocessor::new(mdc).unwrap();
        assert!(
            preprocessor.media_loader.is_none(),
            "default frontend path must not decode media"
        );
        match Arc::try_unwrap(preprocessor) {
            Ok(preprocessor) => preprocessor,
            Err(_) => panic!("test preprocessor unexpectedly shared"),
        }
    }

    fn inline_data_url() -> String {
        "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8z8BQDwAEhQGAhKmMIQAAAABJRU5ErkJggg=="
            .to_string()
    }

    #[tokio::test]
    async fn deepseek_v41_display_name_fallback_orders_tool_media() {
        use crate::common::checked_file::CheckedFile;
        use crate::model_card::ModelInfoType;

        let mut mdc = ModelDeploymentCard::load_from_disk(
            "tests/data/sample-models/mock-llama-3.1-8b-instruct",
            None,
        )
        .unwrap();
        let dir = tempfile::tempdir().unwrap();
        let config = dir.path().join("config.json");
        std::fs::write(
            &config,
            r#"{"architectures":[],"model_type":"","eos_token_id":128009}"#,
        )
        .unwrap();
        mdc.model_info = Some(ModelInfoType::HfConfigJson(
            CheckedFile::from_disk(&config).unwrap(),
        ));
        mdc.display_name = "DeepSeek-V4.1-Flash".into();
        let preprocessor = OpenAIPreprocessor::new(mdc).unwrap();
        let first = inline_data_url().replacen("image/png", "image/png;name=a", 1);
        let second = inline_data_url().replacen("image/png", "image/png;name=b", 1);
        let request: NvCreateChatCompletionRequest = serde_json::from_value(serde_json::json!({
            "model":"alias", "messages":[
                {"role":"assistant","tool_calls":[
                    {"id":"a","type":"function","function":{"name":"image","arguments":"{}"}},
                    {"id":"b","type":"function","function":{"name":"image","arguments":"{}"}}
                ]},
                {"role":"tool","tool_call_id":"b","content":[
                    {"type":"image_url","image_url":{"url":second}}
                ]},
                {"role":"tool","tool_call_id":"a","content":[
                    {"type":"image_url","image_url":{"url":first}}
                ]}
            ]
        }))
        .unwrap();
        let mut builder = PreprocessedRequestBuilder::default();
        builder
            .model("alias".into())
            .token_ids(Vec::new())
            .stop_conditions(Default::default())
            .sampling_options(Default::default())
            .output_options(Default::default());
        preprocessor
            .gather_multi_modal_data_with_image_tokens(&request, &mut builder, None, &[])
            .await
            .unwrap();
        let result = builder.build().unwrap();
        let media = &result.multi_modal_data.unwrap()["image_url"];
        let urls: Vec<_> = media
            .iter()
            .map(|image| match image {
                MultimodalData::Url(url) => url.as_str(),
                other => panic!("expected URL, got {other:?}"),
            })
            .collect();
        assert_eq!(urls, [first.as_str(), second.as_str()]);
    }

    #[tokio::test]
    async fn continue_final_message_rejects_reordered_tool_results() {
        use crate::engines::ValidateRequest;
        use dynamo_renderer::deepseek::v41::DeepSeekV41Formatter;

        let mut preprocessor = test_preprocessor();
        preprocessor.formatter = Arc::new(DeepSeekV41Formatter);
        let image = inline_data_url();
        let mut request: NvCreateChatCompletionRequest = serde_json::from_value(serde_json::json!({
            "model":"alias", "continue_final_message":true, "add_generation_prompt":false,
            "reasoning_effort":"none", "messages":[
                {"role":"assistant","tool_calls":[
                    {"id":"a","type":"function","function":{"name":"image","arguments":"{}"}},
                    {"id":"b","type":"function","function":{"name":"image","arguments":"{}"}}
                ]},
                {"role":"tool","tool_call_id":"b","content":[
                    {"type":"image_url","image_url":{"url":image}}, {"type":"text","text":"Result B"}
                ]},
                {"role":"tool","tool_call_id":"a","content":[
                    {"type":"image_url","image_url":{"url":image}}, {"type":"text","text":"Result A"}
                ]}
            ]
        })).unwrap();
        ValidateRequest::validate(&request).unwrap();
        let error = preprocessor
            .preprocess_request(&request, None)
            .await
            .unwrap_err();
        assert!(
            format!("{error:#}").contains("Cannot continue the final message"),
            "{error:#}"
        );
        assert_eq!(
            error.downcast_ref::<DynamoError>().unwrap().error_type(),
            ErrorType::InvalidArgument
        );

        request.inner.messages.swap(1, 2);
        let ordered = preprocessor.apply_template(&request).unwrap().unwrap();
        assert_eq!(ordered.as_str().matches("<｜deepseek_image｜>").count(), 2);
        assert!(ordered.as_str().ends_with("Result B"));

        request.inner.messages.swap(1, 2);
        request.inner.messages.push(
            serde_json::from_value(serde_json::json!({
                "role":"assistant","content":"Answer:"
            }))
            .unwrap(),
        );
        let assistant = preprocessor.apply_template(&request).unwrap().unwrap();
        assert_eq!(
            assistant.as_str().matches("<｜deepseek_image｜>").count(),
            2
        );
        assert!(assistant.as_str().ends_with("Answer:"));

        request.inner.messages.pop();
        request.common.continue_final_message = Some(false);
        let ordinary = preprocessor.apply_template(&request).unwrap().unwrap();
        assert_eq!(ordinary.as_str().matches("<｜deepseek_image｜>").count(), 2);
    }

    #[tokio::test]
    async fn extra_args_messages_omit_inline_data_when_multi_modal_data_present() {
        let preprocessor = test_preprocessor();
        let data_url = inline_data_url();
        let second_data_url = format!("{data_url}QQ");
        let https_url = "https://example.com/img.png";
        let request: NvCreateChatCompletionRequest = serde_json::from_value(serde_json::json!({
            "model": "test-model",
            "messages": [{
                "role": "user",
                "content": [
                    {"type": "text", "text": "describe"},
                    {"type": "image_url", "image_url": {"url": data_url}},
                    {"type": "image_url", "image_url": {"url": second_data_url}},
                    {"type": "image_url", "image_url": {"url": https_url}}
                ]
            }],
            "max_tokens": 1
        }))
        .unwrap();

        let (preprocessed, _, _) = preprocessor
            .preprocess_request(&request, None)
            .await
            .unwrap();

        let media = preprocessed
            .multi_modal_data
            .as_ref()
            .expect("single media copy lives in multi_modal_data");
        let images = media.get("image_url").expect("image_url slot");
        assert_eq!(images.len(), 3);
        match &images[0] {
            MultimodalData::Url(url) => assert_eq!(url.as_str(), data_url),
            other => panic!("expected Url for inline image, got {other:?}"),
        }
        match &images[1] {
            MultimodalData::Url(url) => assert_eq!(url.as_str(), second_data_url),
            other => panic!("expected Url for second inline image, got {other:?}"),
        }
        match &images[2] {
            MultimodalData::Url(url) => assert_eq!(url.as_str(), https_url),
            other => panic!("expected Url for HTTP image, got {other:?}"),
        }

        let extra_args = preprocessed
            .extra_args
            .as_ref()
            .expect("chat-template extras must remain");
        let parts = extra_args["messages"][0]["content"]
            .as_array()
            .expect("message content parts");
        assert_eq!(parts[0]["text"], "describe");
        assert_eq!(parts[1]["image_url"]["url"], "");
        assert_eq!(parts[2]["image_url"]["url"], "");
        assert_eq!(parts[3]["image_url"]["url"], https_url);
        assert!(
            extra_args.get("formatted_prompt").is_some(),
            "LLaVA / TRT-LLM template path needs formatted_prompt"
        );
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::protocols::common::extensions::{AGENT_CONTEXT_CONTEXT_KEY, AgentContext};
    use crate::protocols::common::preprocessor::MultimodalData;
    use crate::protocols::common::{OutputOptions, SamplingOptions, StopConditions};
    use dynamo_protocols::types::{
        ChatChoiceStream, ChatCompletionStreamResponseDelta, CreateChatCompletionStreamResponse,
        FinishReason, Role,
    };
    use std::path::PathBuf;

    #[tokio::test]
    async fn eight_family_routes_and_tool_suppression() {
        use crate::protocols::openai::chat_completions::unified_parser;
        let card = ModelDeploymentCard::load_from_disk(
            "tests/data/sample-models/mock-llama-3.1-8b-instruct",
            None,
        )
        .unwrap();
        let mut preprocessor = match Arc::try_unwrap(OpenAIPreprocessor::new(card).unwrap()) {
            Ok(value) => value,
            Err(_) => panic!("unexpected shared preprocessor"),
        };
        for (tool, reason, family) in [
            (Some("qwen3_coder"), Some("qwen3"), "qwen3"),
            (Some("kimi_k2"), Some("kimi_k25"), "kimi_k2"),
            (Some("kimi-k3"), Some("kimi-k3"), "kimi_k3"),
            (Some("deepseekv4"), Some("deepseek-v4"), "deepseek_v4"),
            (Some("deepseek_v41"), Some("deepseek_v41"), "deepseek_v41"),
            (Some("gemma-4"), Some("gemma4"), "gemma4"),
            (Some("glm47"), Some("glm45"), "glm47"),
            (Some("muse"), None, "muse_glimmer"),
            (None, Some("muse_glimmer"), "muse_glimmer"),
        ] {
            preprocessor.tool_call_parser = tool.map(str::to_string);
            preprocessor.runtime_config.reasoning_parser = reason.map(str::to_string);
            if matches!(family, "gemma4" | "muse_glimmer") {
                let prompt = if family == "gemma4" {
                    "<|channel>"
                } else {
                    "assistant to=self<|message|>"
                };
                assert!(preprocessor.configured_prompt_prefill(Some(prompt)).unified);
            }
            for choice in [
                serde_json::Value::Null,
                serde_json::json!("auto"),
                serde_json::json!("none"),
                serde_json::json!("required"),
                serde_json::json!({"type":"function","function":{"name":"write_file"}}),
            ] {
                let request: NvCreateChatCompletionRequest = serde_json::from_value(serde_json::json!({"model":"test", "messages":[{"role":"user","content":"test"}], "tool_choice":choice, "tools":[{"type":"function","function":{"name":"write_file","parameters":{"type":"object","properties":{}}}}]})).unwrap();
                let constraint = crate::preprocessor::tool_choice::guided_tool_constraint(
                    &request, tool, reason, false,
                )
                .unwrap();
                let route = preprocessor
                    .tool_processing_route(&request, &constraint)
                    .unwrap();
                if let Some(expected) = unified_parser::selected_family(tool, reason) {
                    assert!(
                        matches!(route, ToolProcessingRoute::Unified(actual) if actual == expected),
                        "{family} {choice}"
                    );
                } else if family == "muse_glimmer"
                    && (choice.is_null() || choice == "auto" || choice == "none")
                {
                    assert!(matches!(route, ToolProcessingRoute::MuseUnified(_)));
                } else {
                    assert!(!matches!(route, ToolProcessingRoute::Unified(_)));
                }
            }
            if unified_parser::selected_family(tool, reason).is_none() {
                continue;
            }
            for no_tools in [false, true] {
                let mut request = serde_json::json!({"model":"test","messages":[{"role":"user","content":"test"}],"tool_choice":"none"});
                if no_tools {
                    request.as_object_mut().unwrap().remove("tool_choice");
                } else {
                    request["tools"] = serde_json::json!([{"type":"function","function":{"name":"write_file","parameters":{"type":"object","properties":{}}}}]);
                }
                let request: NvCreateChatCompletionRequest =
                    serde_json::from_value(request).unwrap();
                let source: NvCreateChatCompletionStreamResponse = serde_json::from_value(serde_json::json!({"id":"test","object":"chat.completion.chunk","created":0,"model":"test","choices":[{"index":0,"delta":{"tool_calls":[{"index":0,"id":"call-1","type":"function","function":{"name":"write_file","arguments":"{}"}}]},"finish_reason":"tool_calls"}]})).unwrap();
                let result = preprocessor
                    .postprocessor_parsing_stream(
                        futures::stream::iter([Annotated::from_data(source)]),
                        &request,
                        false,
                        false,
                    )
                    .unwrap()
                    .collect::<Vec<_>>()
                    .await;
                assert!(
                    result
                        .iter()
                        .filter_map(|r| r.data.as_ref())
                        .flat_map(|r| &r.inner.choices)
                        .all(|c| c.delta.tool_calls.is_none()
                            && c.finish_reason != Some(FinishReason::ToolCalls)),
                    "{family} no_tools={no_tools}"
                );
            }
        }
    }

    #[tokio::test]
    async fn eight_family_request_initialization() {
        use crate::protocols::openai::chat_completions::unified_parser;
        for (family, native) in unified_parser::tests::eight_family_calls("é initialized") {
            let (tool, reason) = match family {
                "qwen3" => ("qwen3_coder", "qwen3"),
                "kimi_k2" => ("kimi_k2", "kimi_k25"),
                "glm47" => ("glm47", "glm45"),
                other => (other, other),
            };
            if unified_parser::selected_family(Some(tool), Some(reason)).is_none() {
                continue;
            }
            let card = ModelDeploymentCard::load_from_disk(
                "tests/data/sample-models/mock-llama-3.1-8b-instruct",
                None,
            )
            .unwrap();
            let mut preprocessor = match Arc::try_unwrap(OpenAIPreprocessor::new(card).unwrap()) {
                Ok(value) => value,
                Err(_) => panic!("unexpected shared preprocessor"),
            };
            preprocessor.tool_call_parser = Some(tool.into());
            preprocessor.runtime_config.reasoning_parser = Some(reason.into());
            let (open, close) = unified_parser::tests::reasoning_markers(family);
            for thinking in [false, true] {
                for prefilled in [false, true] {
                    for choice in [
                        serde_json::Value::Null,
                        serde_json::json!("auto"),
                        serde_json::json!("required"),
                        serde_json::json!({"type":"function","function":{"name":"write_file"}}),
                    ] {
                        let request: NvCreateChatCompletionRequest = serde_json::from_value(serde_json::json!({"model":"test","messages":[{"role":"user","content":"write it"}], "chat_template_kwargs":{"thinking":thinking},"tool_choice":choice,"tools":[{"type":"function","function":{"name":"write_file","parameters":{"type":"object","properties":{"count":{"type":"integer"},"content":{"type":"string"}}}}}]})).unwrap();
                        let constraint = crate::preprocessor::tool_choice::guided_tool_constraint(
                            &request,
                            Some(tool),
                            Some(reason),
                            false,
                        )
                        .unwrap();
                        let args = serde_json::json!({"count":2,"content":"é initialized"});
                        let payload = match &constraint {
                            crate::protocols::openai::GuidedToolConstraint::GuidedJsonNamed {
                                ..
                            } => args.to_string(),
                            crate::protocols::openai::GuidedToolConstraint::GuidedJsonRequired => {
                                serde_json::json!([{"name":"write_file","arguments":args}])
                                    .to_string()
                            }
                            _ => native.clone(),
                        };
                        let prompt = if thinking && prefilled {
                            open
                        } else {
                            "assistant"
                        };
                        let injected = preprocessor.configured_prompt_prefill(Some(prompt));
                        assert_eq!(injected.unified, thinking && prefilled, "{family}");
                        let raw = if thinking {
                            format!(
                                "{}private{close}{payload}",
                                if prefilled { "" } else { open }
                            )
                        } else {
                            payload
                        };
                        let chunks = raw.chars().map(|ch| Annotated::from_data(serde_json::from_value::<NvCreateChatCompletionStreamResponse>(serde_json::json!({"id":"init","object":"chat.completion.chunk","created":0,"model":"test","choices":[{"index":0,"delta":{"content":ch.to_string()},"finish_reason":null}]})).unwrap())).collect::<Vec<_>>();
                        let result = preprocessor
                            .postprocessor_parsing_stream_with_constraint(
                                futures::stream::iter(chunks),
                                &request,
                                injected,
                                constraint.clone(),
                                preprocessor
                                    .tool_processing_route(&request, &constraint)
                                    .unwrap(),
                            )
                            .unwrap()
                            .collect::<Vec<_>>()
                            .await;
                        let choices = result
                            .iter()
                            .filter_map(|r| r.data.as_ref())
                            .flat_map(|r| &r.inner.choices)
                            .collect::<Vec<_>>();
                        let reasoning = choices
                            .iter()
                            .filter_map(|c| c.delta.reasoning_content.as_deref())
                            .collect::<String>();
                        assert_eq!(
                            reasoning,
                            if thinking { "private" } else { "" },
                            "{family} thinking={thinking} prefilled={prefilled} choice={choice}"
                        );
                        let content = choices
                            .iter()
                            .filter_map(|choice| match &choice.delta.content {
                                Some(ChatCompletionMessageContent::Text(text)) => {
                                    Some(text.as_str())
                                }
                                _ => None,
                            })
                            .collect::<String>();
                        assert_eq!(
                            content,
                            String::new(),
                            "visible reasoning {family}/{choice}"
                        );
                        let arguments = choices
                            .iter()
                            .filter_map(|c| c.delta.tool_calls.as_ref())
                            .flatten()
                            .filter_map(|c| c.function.as_ref()?.arguments.as_deref())
                            .collect::<String>();
                        assert_eq!(
                            serde_json::from_str::<serde_json::Value>(&arguments).unwrap(),
                            args,
                            "{family} {choice}"
                        );
                    }
                }
            }
        }
    }

    #[tokio::test]
    async fn kimi2_plain_response_without_prompt_prefill_stays_content() {
        use crate::protocols::openai::chat_completions::unified_parser;
        let mut card = ModelDeploymentCard::load_from_disk(
            "tests/data/sample-models/mock-llama-3.1-8b-instruct",
            None,
        )
        .unwrap();
        card.runtime_config.tool_call_parser = Some("kimi_k2".into());
        card.runtime_config.reasoning_parser = Some("kimi_k25".into());
        let preprocessor = OpenAIPreprocessor::new(card).unwrap();
        for thinking in [None, Some(true), Some(false)] {
            let mut request = serde_json::json!({"model":"test", "messages":[{"role":"user", "content":"answer"}], "stream":true});
            if let Some(thinking) = thinking {
                request["chat_template_args"] = serde_json::json!({"enable_thinking":thinking});
            }
            let request: NvCreateChatCompletionRequest = serde_json::from_value(request).unwrap();
            let source: NvCreateChatCompletionStreamResponse = serde_json::from_value(serde_json::json!({"id":"plain", "object":"chat.completion.chunk", "created":0,"model":"test","choices":[{"index":0,"delta":{"content":"answer"},"finish_reason":"stop"}]})).unwrap();
            let output = preprocessor
                .postprocessor_parsing_stream_with_constraint(
                    futures::stream::iter([Annotated::from_data(source)]),
                    &request,
                    false.into(),
                    crate::protocols::openai::GuidedToolConstraint::None,
                    ToolProcessingRoute::Unified(unified_parser::KIMI_K2_UNIFIED_FAMILY),
                )
                .unwrap()
                .collect::<Vec<_>>()
                .await;
            let choices = output
                .iter()
                .filter_map(|response| response.data.as_ref())
                .flat_map(|data| &data.inner.choices)
                .collect::<Vec<_>>();
            let text: String = choices
                .iter()
                .filter_map(|choice| match &choice.delta.content {
                    Some(ChatCompletionMessageContent::Text(text)) => Some(text.as_str()),
                    _ => None,
                })
                .collect();
            assert_eq!(text, "answer", "thinking={thinking:?}");
            assert!(
                choices
                    .iter()
                    .all(|choice| choice.delta.reasoning_content.is_none()),
                "thinking={thinking:?}"
            );
        }
    }

    #[tokio::test]
    async fn gemma_prompt_channel_preserves_optional_label_ownership() {
        use crate::protocols::openai::chat_completions::unified_parser;
        if unified_parser::selected_family(Some("gemma4"), Some("gemma4")).is_none() {
            return;
        }
        let card = ModelDeploymentCard::load_from_disk(
            "tests/data/sample-models/mock-llama-3.1-8b-instruct",
            None,
        )
        .unwrap();
        let mut preprocessor = match Arc::try_unwrap(OpenAIPreprocessor::new(card).unwrap()) {
            Ok(value) => value,
            Err(_) => panic!("unexpected shared preprocessor"),
        };
        preprocessor.tool_call_parser = Some("gemma4".into());
        preprocessor.runtime_config.reasoning_parser = Some("gemma4".into());
        let request: NvCreateChatCompletionRequest = serde_json::from_value(
            serde_json::json!({"model":"test","messages":[{"role":"user","content":"answer"}], "chat_template_args": {"enable_thinking": true}}),
        )
        .unwrap();
        for (prompt, raw, expected) in [
            ("<|channel>", "private<channel|>answer", "private"),
            ("<|channel>thought", "\nprivate<channel|>answer", "private"),
            ("<|channel>thou", "ght\nprivate<channel|>answer", "private"),
            (
                "<|channel> ",
                "thought\nprivate<channel|>answer",
                "thought\nprivate",
            ),
            ("<|channel>", "thought\nprivate<channel|>answer", "private"),
            (
                "<|channel>thought\n",
                "thought\nprivate<channel|>answer",
                "thought\nprivate",
            ),
        ] {
            let prefill = preprocessor.configured_prompt_prefill(Some(prompt));
            for split in raw
                .char_indices()
                .map(|(at, _)| at)
                .chain(std::iter::once(raw.len()))
            {
                let chunks = [&raw[..split], &raw[split..]].into_iter().map(|content| Annotated::from_data(serde_json::from_value::<NvCreateChatCompletionStreamResponse>(serde_json::json!({"id":"gemma-init","object":"chat.completion.chunk","created":0,"model":"test","choices":[{"index":0,"delta":{"content":content},"finish_reason":null}]})).unwrap())).collect::<Vec<_>>();
                let result = preprocessor
                    .postprocessor_parsing_stream_with_constraint(
                        futures::stream::iter(chunks),
                        &request,
                        prefill,
                        crate::protocols::openai::GuidedToolConstraint::None,
                        ToolProcessingRoute::Unified("gemma4"),
                    )
                    .unwrap()
                    .collect::<Vec<_>>()
                    .await;
                let choices = result
                    .iter()
                    .filter_map(|r| r.data.as_ref())
                    .flat_map(|r| &r.inner.choices)
                    .collect::<Vec<_>>();
                assert_eq!(
                    choices
                        .iter()
                        .filter_map(|c| c.delta.reasoning_content.as_deref())
                        .collect::<String>(),
                    expected,
                    "prompt={prompt} split={split}"
                );
                assert_eq!(
                    choices
                        .iter()
                        .filter_map(|c| match &c.delta.content {
                            Some(ChatCompletionMessageContent::Text(text)) => Some(text.as_str()),
                            _ => None,
                        })
                        .collect::<String>(),
                    "answer",
                    "prompt={prompt} split={split}"
                );
            }
        }
    }

    struct UnifiedReplayEngine {
        preprocessor: Arc<OpenAIPreprocessor>,
        native: String,
        arguments: String,
        release: Option<Arc<tokio::sync::Semaphore>>,
    }

    #[async_trait]
    impl
        dynamo_runtime::pipeline::AsyncEngine<
            SingleIn<NvCreateChatCompletionRequest>,
            ManyOut<Annotated<NvCreateChatCompletionStreamResponse>>,
            Error,
        > for UnifiedReplayEngine
    {
        async fn generate(
            &self,
            request: SingleIn<NvCreateChatCompletionRequest>,
        ) -> Result<ManyOut<Annotated<NvCreateChatCompletionStreamResponse>>, Error> {
            use dynamo_runtime::pipeline::{AsyncEngineContextProvider, ResponseStream};
            let (mut request, context) = request.transfer(());
            OpenAIPreprocessor::normalize_kimi_k3_named_tool_choice(
                &mut request,
                self.preprocessor.tool_call_parser.as_deref(),
            );
            let mut backend_request = preprocessed_budget_request(None);
            let constraint = self.preprocessor.apply_tool_choice_guided_decoding(
                &request,
                &mut backend_request,
                false,
            )?;
            eprintln!("replay installed tool constraint: {constraint:?}");
            let raw = match &request.inner.tool_choice {
                Some(ChatCompletionToolChoiceOption::Required)
                    if constraint.installs_guided_json() =>
                {
                    format!(
                        "[{{\"name\":\"write_file\",\"arguments\":{}}}]",
                        self.arguments
                    )
                }
                Some(ChatCompletionToolChoiceOption::Named(_))
                    if constraint.installs_guided_json() =>
                {
                    self.arguments.clone()
                }
                _ => self.native.clone(),
            };
            let mut chunks = Vec::new();
            for (index, character) in raw.chars().enumerate() {
                let response = serde_json::from_value(
                    serde_json::json!({"id":"replay", "object":"chat.completion.chunk", "created":0,"model":"replay","choices":[{"index":0,"delta":{"role":if index == 0 {Some("assistant")} else {None},"content":character.to_string()},"finish_reason":null}]}),
                )?;
                chunks.push(Annotated::from_data(response));
            }
            chunks.push(Annotated::from_data(serde_json::from_value(serde_json::json!({"id":"replay","object":"chat.completion.chunk","created":0,"model":"replay","choices":[{"index":0,"delta":{"content":""},"finish_reason":"stop"}]}))?));
            let pauses: Vec<usize> = [" STAGE_TWO ", " END"]
                .into_iter()
                .filter_map(|marker| raw.find(marker).map(|at| raw[..at].chars().count()))
                .collect();
            let release = self.release.clone();
            let source = async_stream::stream! {
                for (index, chunk) in chunks.into_iter().enumerate() {
                    if pauses.contains(&index) && let Some(release) = &release {
                        release.acquire().await.expect("replay barrier closed").forget();
                    }
                    yield chunk;
                }
            };
            let route = self
                .preprocessor
                .tool_processing_route(&request, &constraint)?;
            let parsed = self
                .preprocessor
                .postprocessor_parsing_stream_with_constraint(
                    source,
                    &request,
                    false.into(),
                    constraint,
                    route,
                )?;
            Ok(ResponseStream::new(Box::pin(parsed), context.context()))
        }
    }

    async fn start_unified_replay(
        family: &str,
        native: String,
        arguments: String,
        release: Option<Arc<tokio::sync::Semaphore>>,
    ) -> (
        u16,
        dynamo_runtime::CancellationToken,
        tokio::task::JoinHandle<anyhow::Result<()>>,
    ) {
        use crate::http::service::service_v2::HttpService;
        let (tool, reason) = match family {
            "qwen3" => ("qwen3_coder", "qwen3"),
            "kimi_k2" => ("kimi_k2", "kimi_k25"),
            "glm47" => ("glm47", "glm45"),
            other => (other, other),
        };
        let mut card = ModelDeploymentCard::load_from_disk(
            "tests/data/sample-models/mock-llama-3.1-8b-instruct",
            None,
        )
        .unwrap();
        card.runtime_config.tool_call_parser = Some(tool.into());
        card.runtime_config.reasoning_parser = Some(reason.into());
        card.worker_type = Some(crate::worker_type::WorkerType::Aggregated);
        card.needs.clear();
        let preprocessor = OpenAIPreprocessor::new(card.clone()).unwrap();
        let listener = tokio::net::TcpListener::bind("127.0.0.1:0").await.unwrap();
        let port = listener.local_addr().unwrap().port();
        let service = HttpService::builder()
            .host("127.0.0.1")
            .port(port)
            .enable_chat_endpoints(true)
            .enable_cmpl_endpoints(false)
            .build()
            .unwrap();
        // HTTP aggregation reads parser configuration from the selected worker
        // card, just as discovery does for a deployed model.
        let mut workers = crate::discovery::WorkerSet::new(
            "replay-workers".into(),
            "replay-checksum".into(),
            card,
        );
        workers.chat_engine = Some(Arc::new(UnifiedReplayEngine {
            preprocessor,
            native,
            arguments,
            release,
        }));
        assert!(
            service
                .model_manager()
                .add_worker_set("replay", "replay-workers", workers)
        );
        let cancel = dynamo_runtime::CancellationToken::new();
        let join = service.spawn_with_listener(cancel.clone(), listener).await;
        (port, cancel, join)
    }

    // The backend cannot emit either continuation until the client releases it.
    // This catches transport buffering even when the final aggregate is correct.
    async fn paused_http_tool_progress(guided: bool) {
        use crate::protocols::openai::chat_completions::unified_parser;
        assert!(
            unified_parser::parsers_v2_selected(),
            "run with DYN_PARSER_VERSION=2"
        );
        let client = reqwest::Client::builder().no_proxy().build().unwrap();
        let mut failures = Vec::new();
        for (shape, fields) in unified_parser::tests::write_file_progress_cases() {
            let expected_arguments: serde_json::Value =
                serde_json::from_str(&unified_parser::tests::ordered_arguments(&fields)).unwrap();
            for (family, native) in unified_parser::tests::native_write_file_calls(&fields) {
                for named in if guided {
                    vec![false, true]
                } else {
                    vec![false]
                } {
                    let release = Arc::new(tokio::sync::Semaphore::new(0));
                    let (port, cancel, join) = start_unified_replay(
                        family,
                        native.clone(),
                        unified_parser::tests::ordered_arguments(&fields),
                        Some(release.clone()),
                    )
                    .await;
                    let choice = if !guided {
                        serde_json::json!("auto")
                    } else if named {
                        serde_json::json!({"type":"function","function":{"name":"write_file"}})
                    } else {
                        serde_json::json!("required")
                    };
                    let request = serde_json::json!({"model":"replay","messages":[{"role":"user","content":"write it"}],"stream":true,"tool_choice":choice,"tools":[{"type":"function","function":{"name":"write_file","parameters":{"type":"object","properties":{"count":{"type":"integer"},"path":{"type":"string"},"content":{"type":"string"}}}}}]});
                    let response = client
                        .post(format!("http://127.0.0.1:{port}/v1/chat/completions"))
                        .json(&request)
                        .send()
                        .await
                        .unwrap();
                    assert!(response.status().is_success());
                    let mut source = response.bytes_stream();
                    let mut body = String::new();
                    let mut pending = Vec::new();
                    let mut arguments = String::new();
                    let mut names = String::new();
                    let mut ids = Vec::new();
                    let mut finishes = Vec::new();
                    let mut progress = Vec::new();
                    for expected in ["first content", "second content"] {
                        let reached =
                            tokio::time::timeout(std::time::Duration::from_secs(2), async {
                                while let Some(bytes) = source.next().await {
                                    pending.extend_from_slice(&bytes.unwrap());
                                    while let Some(end) =
                                        pending.iter().position(|byte| *byte == b'\n')
                                    {
                                        let line =
                                            String::from_utf8(pending.drain(..=end).collect())
                                                .unwrap();
                                        body.push_str(&line);
                                        read_replay_delta(
                                            &line,
                                            &mut names,
                                            &mut arguments,
                                            &mut ids,
                                            &mut finishes,
                                        );
                                    }
                                    if names == "write_file" && arguments.contains(expected) {
                                        return true;
                                    }
                                }
                                false
                            })
                            .await
                            .unwrap_or(false);
                        progress.push(reached);
                        release.add_permits(1);
                    }
                    tokio::time::timeout(std::time::Duration::from_secs(10), async {
                        while let Some(bytes) = source.next().await {
                            pending.extend_from_slice(&bytes.unwrap());
                            while let Some(end) = pending.iter().position(|byte| *byte == b'\n') {
                                let line =
                                    String::from_utf8(pending.drain(..=end).collect()).unwrap();
                                body.push_str(&line);
                                read_replay_delta(
                                    &line,
                                    &mut names,
                                    &mut arguments,
                                    &mut ids,
                                    &mut finishes,
                                );
                            }
                        }
                    })
                    .await
                    .unwrap();
                    cancel.cancel();
                    tokio::time::timeout(std::time::Duration::from_secs(5), join)
                        .await
                        .unwrap()
                        .unwrap()
                        .unwrap();
                    assert!(body.contains("[DONE]"));
                    assert_eq!(names, "write_file", "{family}/{choice}");
                    assert_eq!(ids.len(), 1, "{family}/{choice}");
                    assert_eq!(finishes, ["tool_calls"], "{family}/{choice}");
                    assert_eq!(
                        serde_json::from_str::<serde_json::Value>(&arguments).unwrap(),
                        expected_arguments,
                        "{family}/{choice}"
                    );
                    eprintln!(
                        "paused HTTP {family}/{shape}/{choice}: first={} continued={} final=true",
                        progress[0], progress[1]
                    );
                    if progress != [true, true] {
                        failures.push(format!("{family}/{shape}/{choice}: {progress:?}"));
                    }
                }
            }
        }
        assert!(
            failures.is_empty(),
            "paused HTTP progress failed: {failures:?}"
        );
    }

    fn read_replay_delta(
        line: &str,
        names: &mut String,
        arguments: &mut String,
        ids: &mut Vec<String>,
        finishes: &mut Vec<String>,
    ) {
        let Some(data) = line.trim_end().strip_prefix("data: ") else {
            return;
        };
        if data == "[DONE]" {
            return;
        }
        let event: serde_json::Value = serde_json::from_str(data).unwrap();
        for choice in event["choices"].as_array().unwrap() {
            if let Some(finish) = choice["finish_reason"].as_str() {
                finishes.push(finish.into());
            }
            if let Some(calls) = choice["delta"]["tool_calls"].as_array() {
                for call in calls {
                    assert_eq!(call["index"], 0);
                    if let Some(id) = call["id"].as_str() {
                        ids.push(id.into());
                    }
                    if let Some(name) = call["function"]["name"].as_str() {
                        names.push_str(name);
                    }
                    if let Some(value) = call["function"]["arguments"].as_str() {
                        arguments.push_str(value);
                    }
                }
            }
        }
    }

    #[tokio::test]
    #[ignore = "Run explicitly with DYN_PARSER_VERSION=2; measures upstream native buffering"]
    async fn eight_family_paused_native_http_progress() {
        paused_http_tool_progress(false).await;
    }

    #[tokio::test]
    #[ignore = "Run explicitly with DYN_PARSER_VERSION=2; measures installed forced-tool modes"]
    async fn eight_family_paused_forced_http_progress() {
        paused_http_tool_progress(true).await;
    }

    #[tokio::test]
    async fn eight_family_http_sse_replay() {
        use crate::protocols::openai::chat_completions::unified_parser;
        // Run with the experiment enabled in a separate process: its flag is cached.
        if unified_parser::selected_family(Some("qwen3_coder"), Some("qwen3")).is_none() {
            return;
        }
        let client = reqwest::Client::builder().no_proxy().build().unwrap();
        for (family, native) in unified_parser::tests::eight_family_calls("é replay") {
            let (port, cancel, join) = start_unified_replay(
                family,
                native,
                serde_json::json!({"count":2,"content":"é replay"}).to_string(),
                None,
            )
            .await;
            for streaming in [false, true] {
                for choice in [
                    serde_json::Value::Null,
                    serde_json::json!("auto"),
                    serde_json::json!("required"),
                    serde_json::json!({"type":"function","function":{"name":"write_file"}}),
                    serde_json::json!("none"),
                ] {
                    let response = client.post(format!("http://127.0.0.1:{port}/v1/chat/completions")).json(&serde_json::json!({"model":"replay","messages":[{"role":"user","content":"write it"}],"stream":streaming,"tool_choice":choice,"tools":[{"type":"function","function":{"name":"write_file","parameters":{"type":"object","properties":{"count":{"type":"integer"},"content":{"type":"string"}}}}}]})).send().await.unwrap();
                    let status = response.status();
                    let body = response.text().await.unwrap();
                    assert!(status.is_success(), "{family} {choice} {status}: {body}");
                    let mut arguments = String::new();
                    if streaming {
                        assert!(body.contains("[DONE]"), "{family}");
                        for line in body
                            .lines()
                            .filter_map(|line| line.strip_prefix("data: "))
                            .filter(|line| *line != "[DONE]")
                        {
                            let event: serde_json::Value = serde_json::from_str(line).unwrap();
                            if let Some(calls) =
                                event["choices"][0]["delta"]["tool_calls"].as_array()
                            {
                                for call in calls {
                                    arguments.push_str(
                                        call["function"]["arguments"].as_str().unwrap_or(""),
                                    );
                                }
                            }
                        }
                    } else {
                        let result: serde_json::Value = serde_json::from_str(&body).unwrap();
                        if let Some(calls) =
                            result["choices"][0]["message"]["tool_calls"].as_array()
                        {
                            assert_eq!(calls.len(), 1, "{family}");
                            arguments.push_str(calls[0]["function"]["arguments"].as_str().unwrap());
                        }
                    }
                    if choice == "none" {
                        assert!(arguments.is_empty(), "{family}");
                    } else {
                        assert_eq!(
                            serde_json::from_str::<serde_json::Value>(&arguments).unwrap_or_else(
                                |error| panic!(
                                    "{family} {choice} stream={streaming}: {error}; body={body}"
                                )
                            ),
                            serde_json::json!({"count":2,"content":"é replay"}),
                            "{family} {choice}"
                        );
                    }
                }
            }
            cancel.cancel();
            tokio::time::timeout(std::time::Duration::from_secs(5), join)
                .await
                .unwrap()
                .unwrap()
                .unwrap();
        }
    }

    #[test]
    fn deepseek_v41_preserves_markers_and_initializes_backend_reasoning() {
        for (tool, reasoning) in [(Some("deepseek_v41"), None), (None, Some("deepseek_v41"))] {
            assert!(OpenAIPreprocessor::parser_requires_special_tokens(tool, reasoning).unwrap());
        }
        assert_eq!(
            OpenAIPreprocessor::prompt_injected_reasoning_ended_arg(
                Some("deepseek_v41"),
                Some("<｜Assistant｜><think>"),
            ),
            Some(false)
        );
        assert_eq!(
            OpenAIPreprocessor::prompt_injected_reasoning_ended_arg(
                Some("deepseek_v41"),
                Some("<｜Assistant｜></think>"),
            ),
            None
        );
        let disabled = HashMap::from([("thinking".to_string(), serde_json::json!(false))]);
        assert!(OpenAIPreprocessor::is_reasoning_disabled_by_request(
            Some("deepseek_v41"),
            Some(&disabled)
        ));
    }

    #[test]
    fn lifecycle_preprocessing_requires_frontend_capture() {
        const CHILD: &str = "DYNAMO_PREPROCESSOR_LIFECYCLE_TEST_CHILD";
        if std::env::var_os(CHILD).is_none() {
            // Enable the process-cached knob without racing other tests.
            let output = crate::test_utils::isolated_command(
                "preprocessor::tests::lifecycle_preprocessing_requires_frontend_capture",
            )
            .env(CHILD, "1")
            .env("DYN_LIFECYCLE_TRACE_ENABLED", "true")
            .output()
            .unwrap();
            crate::test_utils::assert_isolated_success(&output);
            return;
        }

        assert!(
            LifecycleTrace::from_request_id_with_role(
                "knob-probe",
                dynamo_runtime::telemetry::LifecycleOperationRole::Worker,
            )
            .is_enabled()
        );
        let mut context = PipelineContext::new(());
        assert!(!preprocessing_lifecycle(&context).is_enabled());
        context.insert(LIFECYCLE_TRACE_CONTEXT_KEY, LifecycleTrace::new(false));
        assert!(!preprocessing_lifecycle(&context).is_enabled());
        context.insert(LIFECYCLE_TRACE_CONTEXT_KEY, LifecycleTrace::new(true));
        assert!(preprocessing_lifecycle(&context).is_enabled());
    }

    #[test]
    fn guided_tool_streaming_release_only_when_guided_json_and_not_rolled_back() {
        assert!(
            OpenAIPreprocessor::guided_tool_streaming_release(true, false),
            "a forced tool_choice with an installed grammar releases incrementally by default"
        );
        assert!(
            !OpenAIPreprocessor::guided_tool_streaming_release(true, true),
            "DYN_ENABLE_GUIDED_TOOL_STREAMING=false must fall back to buffer-to-completion \
             even when guided JSON is installed"
        );
        assert!(
            !OpenAIPreprocessor::guided_tool_streaming_release(false, false),
            "no installed grammar means nothing to release incrementally, rollback or not"
        );
        assert!(
            !OpenAIPreprocessor::guided_tool_streaming_release(false, true),
            "no installed grammar means nothing to release incrementally, rollback or not"
        );
    }

    fn parser_route_test_request(
        tool_choice: ChatCompletionToolChoiceOption,
    ) -> NvCreateChatCompletionRequest {
        NvCreateChatCompletionRequest {
            inner: dynamo_protocols::types::CreateChatCompletionRequestArgs::default()
                .model("test")
                .messages(vec![dynamo_protocols::types::ChatCompletionRequestMessage::User(
                    dynamo_protocols::types::ChatCompletionRequestUserMessage {
                        content: dynamo_protocols::types::ChatCompletionRequestUserMessageContent::Text(
                            "test".to_string(),
                        ),
                        name: None,
                    },
                )])
                .tools(vec![dynamo_protocols::types::ChatCompletionTool {
                    r#type: dynamo_protocols::types::ChatCompletionToolType::Function,
                    function: dynamo_protocols::types::FunctionObject {
                        name: "get_weather".to_string(),
                        description: None,
                        parameters: None,
                        strict: None,
                    },
                }])
                .tool_choice(tool_choice)
                .build()
                .unwrap(),
            common: Default::default(),
            nvext: None,
            chat_template_args: None,
            thinking: None,
            thinking_token_budget: None,
            media_io_kwargs: None,
            return_tokens_as_token_ids: None,
            unsupported_fields: Default::default(),
        }
    }

    // Dynamo chooses the route after rendering and installing guidance. The parser
    // corpus cannot express this prompt-to-route handoff or the batch consumer.
    #[tokio::test]
    async fn muse_forced_choice_preserves_calls_after_native_prompt_prefill() {
        if crate::test_utils::run_isolated(
            concat!(
                module_path!(),
                "::muse_forced_choice_preserves_calls_after_native_prompt_prefill"
            ),
            &[],
        ) {
            return;
        }
        let model_path = PathBuf::from(env!("CARGO_MANIFEST_DIR"))
            .join("tests/data/sample-models/mock-llama-3.1-8b-instruct");
        let mut card = ModelDeploymentCard::load_from_disk(model_path, None).unwrap();
        card.runtime_config.reasoning_parser = Some("muse_glimmer".to_string());
        let preprocessor = OpenAIPreprocessor::new(card).unwrap();
        let request = parser_route_test_request(ChatCompletionToolChoiceOption::Required);
        let prefill = OpenAIPreprocessor::prompt_reasoning_prefill_for_parsers(
            None,
            Some("muse_glimmer"),
            Some("assistant to=self<|message|>"),
        );
        let raw = r#"[{"name":"get_weather","parameters":{"city":"Tokyo"}}]"#;
        let chunks = [
            serde_json::json!({"id":"test", "object":"chat.completion.chunk", "created":0,
                "model":"test", "choices":[{"index":0,"delta":{"content":raw},"finish_reason":null}]}),
            serde_json::json!({"id":"test", "object":"chat.completion.chunk", "created":0,
                "model":"test", "choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}),
        ].into_iter().map(|chunk| Annotated::from_data(
            serde_json::from_value::<NvCreateChatCompletionStreamResponse>(chunk).unwrap()
        ));
        let output = preprocessor
            .postprocessor_parsing_stream(stream::iter(chunks), &request, prefill, false)
            .unwrap();
        let result =
            crate::protocols::openai::chat_completions::aggregator::DeltaAggregator::apply(
                output,
                crate::protocols::openai::ParsingOptions::default(),
            )
            .await
            .unwrap();
        assert_eq!(
            result.inner.choices[0].finish_reason,
            Some(dynamo_protocols::types::FinishReason::ToolCalls)
        );
        let calls = result.inner.choices[0].message.tool_calls.as_ref().unwrap();
        assert_eq!(calls.len(), 1);
        assert_eq!(calls[0].function.name, "get_weather");
        assert_eq!(
            serde_json::from_str::<serde_json::Value>(&calls[0].function.arguments).unwrap(),
            serde_json::json!({"city":"Tokyo"})
        );
    }

    #[tokio::test]
    async fn muse_prompt_prefill_follows_request_route() {
        const CHILD: &str = "DYNAMO_MUSE_PREFILL_ROUTE_CHILD";
        if std::env::var_os(CHILD).is_none() {
            for version in ["auto", "2"] {
                let output = crate::test_utils::isolated_command(
                    "preprocessor::tests::muse_prompt_prefill_follows_request_route",
                )
                .env(CHILD, version)
                .env(env_llm::DYN_PARSER_VERSION, version)
                .output()
                .unwrap();
                crate::test_utils::assert_isolated_success(&output);
            }
            return;
        }
        let explicit_v2 = std::env::var(CHILD).unwrap() == "2";
        let model_path = PathBuf::from(env!("CARGO_MANIFEST_DIR"))
            .join("tests/data/sample-models/mock-llama-3.1-8b-instruct");
        let named = serde_json::from_value::<ChatCompletionToolChoiceOption>(
            serde_json::json!({"type":"function","function":{"name":"get_weather"}}),
        )
        .unwrap();
        for alias in ["muse_glimmer", "muse"] {
            for (tool, reasoning) in [
                (None, Some(alias)),
                (Some("muse_glimmer"), Some(alias)),
                (Some("muse_glimmer"), None),
            ] {
                for choice in [
                    ChatCompletionToolChoiceOption::Required,
                    named.clone(),
                    ChatCompletionToolChoiceOption::Auto,
                    ChatCompletionToolChoiceOption::None,
                ] {
                    let forced = matches!(
                        choice,
                        ChatCompletionToolChoiceOption::Required
                            | ChatCompletionToolChoiceOption::Named(_)
                    );
                    let request = parser_route_test_request(choice.clone());
                    for structural_tag in [false, true] {
                        let mut card =
                            ModelDeploymentCard::load_from_disk(model_path.clone(), None).unwrap();
                        card.runtime_config.tool_call_parser = tool.map(str::to_string);
                        card.runtime_config.reasoning_parser = reasoning.map(str::to_string);
                        let mut preprocessor = OpenAIPreprocessor::new(card).unwrap();
                        let constraint = crate::preprocessor::tool_choice::guided_tool_constraint(
                            &request,
                            tool,
                            reasoning,
                            structural_tag,
                        )
                        .unwrap();
                        let route = preprocessor
                            .tool_processing_route(&request, &constraint)
                            .unwrap();
                        let unified = explicit_v2 || (!structural_tag && !forced);
                        assert_eq!(
                            matches!(
                                route,
                                ToolProcessingRoute::MuseUnified(_)
                                    | ToolProcessingRoute::Unified(_)
                            ),
                            unified
                        );
                        for (prompt, native_open, generic_open) in [
                            ("assistant to=self<|message|>", true, false),
                            ("assistant<think>", false, true),
                            ("assistant", false, false),
                        ] {
                            // Exercise the same rendered-prompt handoff as generate, without a model.
                            Arc::get_mut(&mut preprocessor).unwrap().formatter =
                                test_prompt_formatter(prompt);
                            let (_, _, prefill) = preprocessor
                                .preprocess_request(&request, None)
                                .await
                                .unwrap();
                            let expected_prefill = if unified {
                                native_open
                            } else if reasoning.is_some() {
                                generic_open
                            } else {
                                native_open
                            };
                            assert_eq!(prefill.for_route(&route), expected_prefill);
                            let body = match (&choice, structural_tag) {
                                (ChatCompletionToolChoiceOption::Required, _)
                                    if !unified || !structural_tag =>
                                {
                                    r#"[{"name":"get_weather","parameters":{"city":"Tokyo"}}]"#
                                }
                                (ChatCompletionToolChoiceOption::Named(_), _)
                                    if !unified || !structural_tag =>
                                {
                                    r#"{"city":"Tokyo"}"#
                                }
                                _ => "answer",
                            };
                            let raw = if !unified && reasoning.is_some() && generic_open {
                                format!("thought</think>{body}")
                            } else if unified && native_open && !forced {
                                format!(
                                    "thought<|eom|><|start|>assistant to=user<|message|>{body}<|eot|>"
                                )
                            } else {
                                body.to_string()
                            };
                            for split in 0..=raw.len() {
                                let input = || {
                                    [Some(&raw[..split]), Some(&raw[split..]), None].into_iter().map(|text| {
                                        Annotated::from_data(serde_json::from_value::<NvCreateChatCompletionStreamResponse>(serde_json::json!({
                                            "id":"test", "object":"chat.completion.chunk", "created":0, "model":"test",
                                            "choices":[{"index":0,"delta":{"content":text},"finish_reason": if text.is_none() { Some("stop") } else { None }}]
                                        })).unwrap())
                                    }).collect::<Vec<_>>()
                                };
                                let actual: Vec<_> = preprocessor
                                    .postprocessor_parsing_stream(
                                        stream::iter(input()),
                                        &request,
                                        prefill,
                                        structural_tag,
                                    )
                                    .unwrap()
                                    .collect()
                                    .await;
                                // Legacy expectations come from the pre-PR Basic <think> contract;
                                // native expectations belong only to unified routes. Explicit booleans
                                // also pin compatibility for existing postprocessor callers.
                                let expected: Vec<_> = preprocessor
                                    .postprocessor_parsing_stream(
                                        stream::iter(input()),
                                        &request,
                                        expected_prefill,
                                        structural_tag,
                                    )
                                    .unwrap()
                                    .collect()
                                    .await;
                                let snapshot = |chunks: &[Annotated<
                                    NvCreateChatCompletionStreamResponse,
                                >]| {
                                    chunks
                                        .iter()
                                        .map(|chunk| {
                                            let mut value = serde_json::to_value(chunk).unwrap();
                                            if let Some(data) = value.get_mut("data") {
                                                for choice in
                                                    data["choices"].as_array_mut().unwrap()
                                                {
                                                    if let Some(calls) =
                                                        choice["delta"]["tool_calls"].as_array_mut()
                                                    {
                                                        for call in calls {
                                                            if call.get("id").is_some() {
                                                                call["id"] = serde_json::json!(
                                                                    "generated-id"
                                                                );
                                                            }
                                                        }
                                                    }
                                                }
                                            }
                                            value
                                        })
                                        .collect::<Vec<_>>()
                                };
                                assert_eq!(
                                    snapshot(&actual),
                                    snapshot(&expected),
                                    "{alias} {choice:?} tag={structural_tag} prompt={prompt} split={split}"
                                );
                                assert!(actual.iter().all(|chunk| chunk.error.is_none()));
                                // TODO: the published legacy Basic parser loses split
                                // </think> markers. Preserve its boolean-path output above, but
                                // qualify call recovery below only for unsplit generic-prefill input.
                                if !unified
                                    && reasoning.is_some()
                                    && generic_open
                                    && split != 0
                                    && split != raw.len()
                                {
                                    continue;
                                }
                                assert!(
                                    actual
                                        .iter()
                                        .filter_map(|chunk| chunk.data.as_ref())
                                        .flat_map(|chunk| &chunk.inner.choices)
                                        .any(|choice| choice.finish_reason.is_some()),
                                    "missing finish: {alias} {choice:?} tag={structural_tag} prompt={prompt} split={split} chunks={:?}",
                                    snapshot(&actual)
                                );
                                let batch = crate::protocols::openai::chat_completions::aggregator::DeltaAggregator::apply(
                                    stream::iter(actual), crate::protocols::openai::ParsingOptions::default(),
                                ).await.unwrap();
                                if forced && !structural_tag {
                                    let calls =
                                        batch.inner.choices[0].message.tool_calls.as_ref().unwrap();
                                    assert_eq!(calls.len(), 1);
                                    assert_eq!(calls[0].function.name, "get_weather");
                                    assert_eq!(
                                        serde_json::from_str::<serde_json::Value>(
                                            &calls[0].function.arguments
                                        )
                                        .unwrap(),
                                        serde_json::json!({"city":"Tokyo"})
                                    );
                                    assert_eq!(
                                        batch.inner.choices[0].finish_reason,
                                        Some(dynamo_protocols::types::FinishReason::ToolCalls)
                                    );
                                }
                            }
                        }
                    }
                }
            }
        }
    }

    #[test]
    fn all_requested_families_use_unified_serving_route_for_every_tool_policy() {
        const CHILD: &str = "DYNAMO_ALL_UNIFIED_SERVING_ROUTE_CHILD";
        if std::env::var_os(CHILD).is_none() {
            for (version, experimental) in [(Some("2"), None), (None, Some("true"))] {
                let mut command = crate::test_utils::isolated_command(
                    "preprocessor::tests::all_requested_families_use_unified_serving_route_for_every_tool_policy",
                );
                command.arg("--test-threads=1").env(CHILD, "1");
                if let Some(version) = version {
                    command.env(env_llm::DYN_PARSER_VERSION, version);
                } else {
                    command.env_remove(env_llm::DYN_PARSER_VERSION);
                }
                if let Some(experimental) = experimental {
                    command.env(env_llm::DYN_ENABLE_EXPERIMENTAL_PARSERS_V2, experimental);
                } else {
                    command.env_remove(env_llm::DYN_ENABLE_EXPERIMENTAL_PARSERS_V2);
                }
                let output = command.output().unwrap();
                crate::test_utils::assert_isolated_success(&output);
            }
            return;
        }
        let model_path = PathBuf::from(env!("CARGO_MANIFEST_DIR"))
            .join("tests/data/sample-models/mock-llama-3.1-8b-instruct");
        for (tool, reasoning, family) in [
            ("deepseek_v4", "deepseek_v4", "deepseek_v4"),
            ("deepseek_v41", "deepseek_v41", "deepseek_v41"),
            ("gemma4", "gemma4", "gemma4"),
            ("glm47", "glm45", "glm47"),
            ("kimi_k2", "kimi_k25", "kimi_k2"),
            ("kimi_k3", "kimi_k3", "kimi_k3"),
            ("muse", "muse", "muse_glimmer"),
            ("qwen3_coder", "qwen3", "qwen3"),
        ] {
            let mut card = ModelDeploymentCard::load_from_disk(model_path.clone(), None).unwrap();
            card.runtime_config.tool_call_parser = Some(tool.to_string());
            card.runtime_config.reasoning_parser = Some(reasoning.to_string());
            let preprocessor = OpenAIPreprocessor::new(card).unwrap();
            for choice in [
                ChatCompletionToolChoiceOption::Auto,
                ChatCompletionToolChoiceOption::None,
                ChatCompletionToolChoiceOption::Required,
                serde_json::from_value(
                    serde_json::json!({"type":"function","function":{"name":"get_weather"}}),
                )
                .unwrap(),
            ] {
                for structural_tag in [false, true] {
                    let mut request = parser_route_test_request(choice.clone());
                    for has_tools in [true, false] {
                        if !has_tools {
                            request.inner.tools = None;
                        }
                        let constraint = crate::preprocessor::tool_choice::guided_tool_constraint(
                            &request,
                            Some(tool),
                            Some(reasoning),
                            structural_tag,
                        );
                        if !has_tools
                            && !matches!(
                                choice,
                                ChatCompletionToolChoiceOption::Auto
                                    | ChatCompletionToolChoiceOption::None
                            )
                        {
                            // Forced choices require declared tools at request validation.
                            continue;
                        }
                        let constraint = constraint.unwrap();
                        assert_eq!(
                            preprocessor
                                .tool_processing_route(&request, &constraint)
                                .unwrap(),
                            ToolProcessingRoute::Unified(family),
                            "{tool}/{reasoning} {choice:?} structural_tag={structural_tag} has_tools={has_tools}"
                        );
                    }
                }
            }
        }
    }

    #[test]
    fn qwen3_coder_uses_unified_route_only_after_explicit_opt_in() {
        const CHILD: &str = "DYNAMO_PARSER_VERSION_TEST_QWEN3_OPT_IN";
        const MODE: &str = "DYNAMO_PARSER_VERSION_TEST_QWEN3_MODE";
        if std::env::var_os(CHILD).is_none() {
            for (mode, experimental) in [("default", None), ("opt-in", Some("true"))] {
                let mut command = crate::test_utils::isolated_command(
                    "preprocessor::tests::qwen3_coder_uses_unified_route_only_after_explicit_opt_in",
                );
                command
                    .arg("--test-threads=1")
                    .env(CHILD, "1")
                    .env(MODE, mode)
                    .env_remove(env_llm::DYN_PARSER_VERSION);
                if let Some(value) = experimental {
                    command.env(env_llm::DYN_ENABLE_EXPERIMENTAL_PARSERS_V2, value);
                } else {
                    command.env_remove(env_llm::DYN_ENABLE_EXPERIMENTAL_PARSERS_V2);
                }
                let output = command.output().unwrap();
                crate::test_utils::assert_isolated_success(&output);
            }
            return;
        }

        let model_path = PathBuf::from(env!("CARGO_MANIFEST_DIR"))
            .join("tests/data/sample-models/mock-llama-3.1-8b-instruct");
        let mut card = ModelDeploymentCard::load_from_disk(model_path, None).unwrap();
        card.runtime_config.tool_call_parser = Some("qwen3_coder".to_string());
        let preprocessor = OpenAIPreprocessor::new(card).unwrap();
        let request = parser_route_test_request(ChatCompletionToolChoiceOption::Auto);
        let constraint = crate::preprocessor::tool_choice::guided_tool_constraint(
            &request,
            Some("qwen3_coder"),
            None,
            false,
        )
        .unwrap();
        let route = preprocessor
            .tool_processing_route(&request, &constraint)
            .unwrap();
        match std::env::var(MODE).as_deref() {
            Ok("default") => assert_eq!(
                route,
                ToolProcessingRoute::LegacyJail(Some("qwen3_coder".to_string()))
            ),
            Ok("opt-in") => assert_eq!(route, ToolProcessingRoute::Unified("qwen3")),
            value => panic!("unexpected test mode: {value:?}"),
        }
    }

    #[test]
    fn experimental_flag_uses_explicit_v2_compatibility_validation() {
        if crate::test_utils::run_isolated(
            concat!(
                module_path!(),
                "::experimental_flag_uses_explicit_v2_compatibility_validation"
            ),
            &[(env_llm::DYN_ENABLE_EXPERIMENTAL_PARSERS_V2, "true")],
        ) {
            return;
        }
        for (tool, reasoning) in [("hermes", None), ("qwen3_coder", Some("hermes"))] {
            let model_path = PathBuf::from(env!("CARGO_MANIFEST_DIR"))
                .join("tests/data/sample-models/mock-llama-3.1-8b-instruct");
            let mut card = ModelDeploymentCard::load_from_disk(model_path, None).unwrap();
            card.model_type = crate::model_type::ModelType::Chat;
            card.runtime_config.tool_call_parser = Some(tool.to_string());
            card.runtime_config.reasoning_parser = reasoning.map(str::to_string);
            let error = OpenAIPreprocessor::new(card)
                .err()
                .expect("unsupported V2 configuration must fail");
            assert!(error.to_string().contains("DYN_PARSER_VERSION=2"));
        }
    }

    #[test]
    fn family_defaults_keep_original_routes_for_every_request_mode() {
        const CHILD: &str = "DYNAMO_PARSER_FAMILY_DEFAULT_MODE_CHILD";
        if std::env::var_os(CHILD).is_none() {
            let output = crate::test_utils::isolated_command(
                "preprocessor::tests::family_defaults_keep_original_routes_for_every_request_mode",
            )
            .env(CHILD, "1")
            .env_remove(env_llm::DYN_PARSER_VERSION)
            .env_remove(env_llm::DYN_ENABLE_EXPERIMENTAL_PARSERS_V2)
            .output()
            .unwrap();
            crate::test_utils::assert_isolated_success(&output);
            return;
        }

        let model_path = PathBuf::from(env!("CARGO_MANIFEST_DIR"))
            .join("tests/data/sample-models/mock-llama-3.1-8b-instruct");
        let families = [
            ("deepseek_v4", "deepseek_v4", "deepseek_v4"),
            ("deepseek_v41", "deepseek_v41", "deepseek_v41"),
            ("gemma4", "gemma4", "gemma4"),
            ("glm47", "glm45", "glm47"),
            ("kimi_k2", "kimi_k25", "kimi_k2"),
            ("kimi_k3", "kimi_k3", "kimi_k3"),
            ("muse", "muse", "muse_glimmer"),
            ("qwen3_coder", "qwen3", "qwen3"),
        ];
        let choices = [
            ChatCompletionToolChoiceOption::Auto,
            ChatCompletionToolChoiceOption::None,
            ChatCompletionToolChoiceOption::Required,
            serde_json::from_value(
                serde_json::json!({"type":"function","function":{"name":"get_weather"}}),
            )
            .unwrap(),
        ];

        for (tool, reasoning, family) in families {
            let mut card = ModelDeploymentCard::load_from_disk(model_path.clone(), None).unwrap();
            card.runtime_config.tool_call_parser = Some(tool.to_string());
            card.runtime_config.reasoning_parser = Some(reasoning.to_string());
            let preprocessor = OpenAIPreprocessor::new(card).unwrap();
            let is_default_v2 = family == "deepseek_v41";
            for choice in &choices {
                for structural_tag in [false, true] {
                    for has_tools in [true, false] {
                        let mut request = parser_route_test_request(choice.clone());
                        if !has_tools {
                            request.inner.tools = None;
                        }
                        let constraint = crate::preprocessor::tool_choice::guided_tool_constraint(
                            &request,
                            Some(tool),
                            Some(reasoning),
                            structural_tag,
                        );
                        let Ok(constraint) = constraint else {
                            continue;
                        };
                        let route = preprocessor
                            .tool_processing_route(&request, &constraint)
                            .unwrap();
                        let is_original_muse_unified = family == "muse_glimmer"
                            && !structural_tag
                            && matches!(
                                choice,
                                ChatCompletionToolChoiceOption::Auto
                                    | ChatCompletionToolChoiceOption::None
                            );
                        if is_original_muse_unified {
                            assert_eq!(
                                route,
                                ToolProcessingRoute::MuseUnified("muse_glimmer"),
                                "{tool}/{reasoning} {choice:?} structural_tag={structural_tag} has_tools={has_tools}"
                            );
                            continue;
                        }
                        if is_default_v2 {
                            assert_eq!(
                                route,
                                ToolProcessingRoute::Unified(family),
                                "{tool}/{reasoning} {choice:?} structural_tag={structural_tag} has_tools={has_tools}"
                            );
                        } else {
                            assert!(
                                !matches!(
                                    route,
                                    ToolProcessingRoute::Unified(_)
                                        | ToolProcessingRoute::MuseUnified(_)
                                ),
                                "{tool}/{reasoning} {choice:?} structural_tag={structural_tag} has_tools={has_tools}: {route:?}"
                            );
                        }
                    }
                }
            }
        }
    }

    #[test]
    fn explicit_v2_rejects_modes_that_require_the_v1_jail() {
        if crate::test_utils::run_isolated(
            concat!(
                module_path!(),
                "::explicit_v2_rejects_modes_that_require_the_v1_jail"
            ),
            &[(env_llm::DYN_PARSER_VERSION, "2")],
        ) {
            return;
        }

        let model_path = PathBuf::from(env!("CARGO_MANIFEST_DIR"))
            .join("tests/data/sample-models/mock-llama-3.1-8b-instruct");
        for (parser, structural_tag) in [
            ("muse_glimmer", false),
            ("muse_glimmer", true),
            ("qwen3_coder", false),
            ("qwen3_coder", true),
        ] {
            let mut card = ModelDeploymentCard::load_from_disk(model_path.clone(), None).unwrap();
            card.runtime_config.tool_call_parser = Some(parser.to_string());
            let preprocessor = OpenAIPreprocessor::new(card).unwrap();
            let request = parser_route_test_request(ChatCompletionToolChoiceOption::Required);
            let constraint = crate::preprocessor::tool_choice::guided_tool_constraint(
                &request,
                Some(parser),
                None,
                structural_tag,
            )
            .unwrap();
            assert_eq!(
                preprocessor
                    .tool_processing_route(&request, &constraint)
                    .unwrap(),
                ToolProcessingRoute::Unified(if parser == "muse_glimmer" {
                    "muse_glimmer"
                } else {
                    "qwen3"
                }),
            );
        }

        let card = ModelDeploymentCard::load_from_disk(model_path, None).unwrap();
        let preprocessor = OpenAIPreprocessor::new(card).unwrap();
        let request = parser_route_test_request(ChatCompletionToolChoiceOption::Required);
        let constraint =
            crate::preprocessor::tool_choice::guided_tool_constraint(&request, None, None, false)
                .unwrap();
        let error = preprocessor
            .tool_processing_route(&request, &constraint)
            .expect_err("explicit v2 must not construct an unconfigured immediate jail");
        let dynamo_error = error
            .downcast_ref::<DynamoError>()
            .expect("request-route errors must retain their InvalidArgument classification");
        assert_eq!(dynamo_error.error_type(), ErrorType::InvalidArgument);
        assert!(
            dynamo_error
                .message()
                .contains("requires the v1 tool-call jail")
        );
        match crate::http::service::error::http_action_for_error(dynamo_error) {
            crate::http::service::error::ClientErrorAction::Respond { status, .. } => {
                assert_eq!(status.as_u16(), 400);
            }
            crate::http::service::error::ClientErrorAction::NoDelivery => {
                panic!("invalid parser-route requests must return an HTTP response");
            }
        }
    }

    #[test]
    fn legacy_jail_rejects_multiple_choices_for_choice_specific_nvext() {
        let engine_data = vec!["engine_data".to_string()];
        let request_level = vec!["timing".to_string(), "worker_id".to_string()];

        assert!(validate_legacy_jail_nvext_choice_count(2, Some(&engine_data), true).is_err());
        assert!(validate_legacy_jail_nvext_choice_count(1, Some(&engine_data), true).is_ok());
        assert!(validate_legacy_jail_nvext_choice_count(2, Some(&request_level), true).is_ok());

        for route in [
            ToolProcessingRoute::Unified("muse_glimmer"),
            ToolProcessingRoute::Unified("qwen3"),
            ToolProcessingRoute::PassThrough,
        ] {
            assert!(
                validate_legacy_jail_nvext_choice_count(
                    2,
                    Some(&engine_data),
                    route.uses_legacy_jail(),
                )
                .is_ok()
            );
        }
    }

    fn chat_stream_chunk(
        index: u32,
        role: Option<Role>,
    ) -> Annotated<NvCreateChatCompletionStreamResponse> {
        #[allow(deprecated)]
        let choice = ChatChoiceStream {
            index,
            delta: ChatCompletionStreamResponseDelta {
                role,
                content: Some(ChatCompletionMessageContent::Text("content".to_string())),
                tool_calls: None,
                function_call: None,
                refusal: None,
                reasoning_content: None,
            },
            finish_reason: None,
            logprobs: None,
        };
        Annotated::from_data(NvCreateChatCompletionStreamResponse {
            inner: CreateChatCompletionStreamResponse {
                id: "test".to_string(),
                choices: vec![choice],
                created: 0,
                model: "test".to_string(),
                system_fingerprint: None,
                object: "chat.completion.chunk".to_string(),
                usage: None,
                service_tier: None,
            },
            nvext: None,
            prompt_logprobs: None,
            llm_metrics: None,
            tool_call_completion: Vec::new(),
        })
    }

    fn reasoning_usage_chunk(
        reasoning: Option<&str>,
        content: Option<&str>,
        chunk_tokens: usize,
    ) -> Annotated<NvCreateChatCompletionStreamResponse> {
        let mut chunk = chat_stream_chunk(0, None);
        let data = chunk.data.as_mut().unwrap();
        data.inner.choices[0].delta.reasoning_content = reasoning.map(str::to_string);
        data.inner.choices[0].delta.content = content
            .map(str::to_string)
            .map(ChatCompletionMessageContent::Text);
        data.llm_metrics = Some(LLMMetricAnnotation {
            chunk_tokens,
            ..Default::default()
        });
        chunk
    }

    fn reasoning_usage_trailer(
        completion_tokens: u32,
        backend_reasoning_tokens: Option<u32>,
    ) -> Annotated<NvCreateChatCompletionStreamResponse> {
        let mut chunk = chat_stream_chunk(0, None);
        let data = chunk.data.as_mut().unwrap();
        data.inner.choices.clear();
        let mut usage = dynamo_protocols::types::CompletionUsage {
            prompt_tokens: 5,
            completion_tokens,
            total_tokens: 5 + completion_tokens,
            prompt_tokens_details: None,
            completion_tokens_details: None,
        };
        if let Some(reasoning_tokens) = backend_reasoning_tokens {
            usage
                .completion_tokens_details
                .get_or_insert_default()
                .reasoning_tokens = Some(reasoning_tokens);
        }
        data.inner.usage = Some(usage);
        data.llm_metrics = Some(LLMMetricAnnotation {
            output_tokens: completion_tokens as usize,
            ..Default::default()
        });
        chunk
    }

    #[tokio::test]
    async fn reasoning_usage_estimator_stamps_shared_chat_usage() {
        let output = annotate_reasoning_usage(stream::iter(vec![
            reasoning_usage_chunk(Some("think"), None, 1),
            reasoning_usage_chunk(Some(" more"), None, 1),
            reasoning_usage_chunk(None, None, 1),
            reasoning_usage_chunk(None, Some("answer"), 1),
            reasoning_usage_trailer(4, None),
        ]))
        .collect::<Vec<_>>()
        .await;

        let usage = output
            .last()
            .and_then(|response| response.data.as_ref())
            .and_then(|chunk| chunk.inner.usage.as_ref())
            .unwrap();
        assert_eq!(
            usage
                .completion_tokens_details
                .as_ref()
                .and_then(|details| details.reasoning_tokens),
            Some(3)
        );
    }

    #[tokio::test]
    async fn reasoning_usage_estimator_documents_mixed_chunk_overcount() {
        let output = annotate_reasoning_usage(stream::iter(vec![
            reasoning_usage_chunk(Some("think"), Some("answer"), 4),
            reasoning_usage_trailer(4, None),
        ]))
        .collect::<Vec<_>>()
        .await;

        let usage = output
            .last()
            .and_then(|response| response.data.as_ref())
            .and_then(|chunk| chunk.inner.usage.as_ref())
            .unwrap();
        assert_eq!(
            usage
                .completion_tokens_details
                .as_ref()
                .and_then(|details| details.reasoning_tokens),
            Some(4),
            "the chunk-granular estimate intentionally attributes the mixed chunk to reasoning"
        );
    }

    #[tokio::test]
    async fn reasoning_usage_estimator_preserves_positive_backend_count() {
        let output = annotate_reasoning_usage(stream::iter(vec![
            reasoning_usage_chunk(Some("two estimated tokens"), None, 2),
            reasoning_usage_trailer(2, Some(1)),
        ]))
        .collect::<Vec<_>>()
        .await;

        let usage = output
            .last()
            .and_then(|response| response.data.as_ref())
            .and_then(|chunk| chunk.inner.usage.as_ref())
            .unwrap();
        assert_eq!(
            usage
                .completion_tokens_details
                .as_ref()
                .and_then(|details| details.reasoning_tokens),
            Some(1)
        );
    }

    #[tokio::test]
    async fn disabled_unified_reasoning_keeps_interleaved_text_order_in_stream_and_batch() {
        use crate::protocols::openai::GuidedToolConstraint;
        use crate::protocols::openai::chat_completions::unified_parser;
        for (family, input) in [
            (
                "kimi_k3",
                "<|open|>response<|sep|>before<|close|>response<|sep|><|open|>think<|sep|>hidden<|close|>think<|sep|><|open|>response<|sep|>after<|close|>response<|sep|>",
            ),
            (
                "muse_glimmer",
                "<|start|>assistant to=user<|message|>before<|eom|><|start|>assistant to=self<|message|>hidden<|eom|><|start|>assistant to=user<|message|>after<|eot|>",
            ),
        ] {
            let batch = unified_parser::parse_complete_with_policy(
                family,
                input,
                &GuidedToolConstraint::None,
                &[],
                unified_parser::UnifiedRequestPolicy {
                    reasoning_disabled: true,
                    ..Default::default()
                },
            )
            .unwrap();
            assert_eq!(batch.text, "beforehiddenafter", "{family}");
            assert!(batch.reasoning.is_empty());
            for split in input.char_indices().map(|(offset, _)| offset) {
                let (first, second) = input.split_at(split);
                let mut terminal = chat_stream_chunk(0, None);
                terminal.data.as_mut().unwrap().inner.choices[0]
                    .delta
                    .content = Some(ChatCompletionMessageContent::Text(second.to_string()));
                let mut first_chunk = chat_stream_chunk(0, None);
                first_chunk.data.as_mut().unwrap().inner.choices[0]
                    .delta
                    .content = Some(ChatCompletionMessageContent::Text(first.to_string()));
                terminal.data.as_mut().unwrap().inner.choices[0].finish_reason =
                    Some(FinishReason::Stop);
                let raw = unified_parser::apply_stream_with_constraint(
                    stream::iter([first_chunk, terminal]),
                    None,
                    GuidedToolConstraint::None,
                    dynamo_parsers_v2::UnifiedParserStartingState::None,
                    family,
                    true,
                );
                let output =
                    OpenAIPreprocessor::apply_unified_response_policies(raw, true, false, true)
                        .collect::<Vec<_>>()
                        .await;
                let mut text = String::new();
                for choice in output
                    .iter()
                    .filter_map(|r| r.data.as_ref())
                    .flat_map(|r| &r.inner.choices)
                {
                    assert!(
                        choice.delta.reasoning_content.is_none(),
                        "{family} split={split}"
                    );
                    if let Some(ChatCompletionMessageContent::Text(t)) = &choice.delta.content {
                        text.push_str(t);
                    }
                }
                assert_eq!(text, batch.text, "{family} split={split}");
            }
        }
    }

    #[tokio::test]
    async fn force_nonempty_deferral_preserves_reasoning_usage() {
        let reasoning = reasoning_usage_chunk(Some("deferred thought"), None, 3);
        let mut terminal = chat_stream_chunk(0, None);
        let terminal_choice = &mut terminal.data.as_mut().unwrap().inner.choices[0];
        terminal_choice.delta.content = None;
        terminal_choice.finish_reason = Some(FinishReason::Stop);

        let output = OpenAIPreprocessor::apply_unified_response_policies(
            stream::iter(vec![reasoning, terminal, reasoning_usage_trailer(3, None)]),
            true,
            true,
            false,
        )
        .collect::<Vec<_>>()
        .await;
        let usage = output
            .last()
            .and_then(|response| response.data.as_ref())
            .and_then(|chunk| chunk.inner.usage.as_ref())
            .expect("held usage trailer");

        assert_eq!(
            usage
                .completion_tokens_details
                .as_ref()
                .and_then(|details| details.reasoning_tokens),
            Some(3),
            "deferral must not erase the parser's reasoning classification"
        );
        assert_eq!(
            output
                .iter()
                .filter_map(|response| response.data.as_ref())
                .filter(|chunk| chunk.llm_metrics.is_some())
                .count(),
            2,
            "one source metric and one usage-trailer metric must survive"
        );
    }

    #[tokio::test]
    async fn test_normalize_chat_stream_roles_recovers_missing_first_role_per_choice() {
        let input = stream::iter(vec![
            // Mirrors a parser releasing buffered content without the role that
            // arrived on the original, swallowed chunk.
            chat_stream_chunk(0, None),
            chat_stream_chunk(1, None),
            chat_stream_chunk(0, Some(Role::Assistant)),
            chat_stream_chunk(1, Some(Role::Assistant)),
        ]);

        let output: Vec<_> = OpenAIPreprocessor::normalize_chat_stream_roles(input)
            .collect()
            .await;
        let roles: Vec<_> = output
            .iter()
            .map(|response| response.data.as_ref().unwrap().inner.choices[0].delta.role)
            .collect();

        assert_eq!(
            roles,
            vec![Some(Role::Assistant), Some(Role::Assistant), None, None,]
        );
    }

    #[tokio::test]
    async fn legacy_jail_preserves_prompt_logprobs_metadata() {
        let expected: crate::protocols::common::llm_backend::PromptLogprobs =
            serde_json::from_value(serde_json::json!([
                null,
                {"17": {"logprob": -0.25, "rank": 1, "decoded_token": " hello"}}
            ]))
            .expect("valid prompt logprobs");
        let mut chunk = terminal_chat_stream_chunk();
        chunk.data.as_mut().unwrap().prompt_logprobs = Some(Arc::new(expected.clone()));

        let output = OpenAIPreprocessor::apply_tool_calling_jail(
            None,
            None,
            None,
            false,
            false,
            stream::iter(vec![chunk]),
        )
        .collect::<Vec<_>>()
        .await;

        assert_eq!(
            output
                .iter()
                .filter_map(|response| response.data.as_ref())
                .find_map(|data| data.prompt_logprobs.as_deref()),
            Some(&expected)
        );
    }

    fn kimi_k3_reasoning_chunk(reasoning: &str) -> Annotated<NvCreateChatCompletionStreamResponse> {
        let mut chunk = chat_stream_chunk(0, Some(Role::Assistant));
        let choice = &mut chunk.data.as_mut().unwrap().inner.choices[0];
        choice.delta.content = None;
        choice.delta.reasoning_content = Some(reasoning.to_string());
        chunk
    }

    fn terminal_chat_stream_chunk() -> Annotated<NvCreateChatCompletionStreamResponse> {
        let mut chunk = chat_stream_chunk(0, None);
        let choice = &mut chunk.data.as_mut().unwrap().inner.choices[0];
        choice.delta.content = None;
        choice.finish_reason = Some(FinishReason::Stop);
        chunk
    }

    fn glm47_stream_chunk(
        content: &str,
        finish_reason: Option<FinishReason>,
    ) -> Annotated<NvCreateChatCompletionStreamResponse> {
        let mut chunk = chat_stream_chunk(0, Some(Role::Assistant));
        let choice = &mut chunk.data.as_mut().unwrap().inner.choices[0];
        choice.delta.content = Some(ChatCompletionMessageContent::Text(content.to_string()));
        choice.finish_reason = finish_reason;
        chunk
    }

    async fn apply_glm47_streaming_with_terminal(
        chunks: &[&str],
        terminal: FinishReason,
    ) -> Vec<Annotated<NvCreateChatCompletionStreamResponse>> {
        let chunks: Vec<String> = chunks.iter().map(|chunk| (*chunk).to_string()).collect();
        let chunk_count = chunks.len();
        OpenAIPreprocessor::apply_tool_calling_jail(
            Some("glm47".to_string()),
            None,
            None,
            false,
            false,
            stream::iter(chunks.into_iter().enumerate().map(move |(index, content)| {
                glm47_stream_chunk(&content, (index + 1 == chunk_count).then_some(terminal))
            })),
        )
        .collect()
        .await
    }

    async fn apply_glm47_streaming_length(
        chunks: &[&str],
    ) -> Vec<Annotated<NvCreateChatCompletionStreamResponse>> {
        apply_glm47_streaming_with_terminal(chunks, FinishReason::Length).await
    }

    fn has_finish_reason(
        output: &[Annotated<NvCreateChatCompletionStreamResponse>],
        reason: FinishReason,
    ) -> bool {
        output
            .iter()
            .flat_map(|response| response.data.iter())
            .flat_map(|data| data.inner.choices.iter())
            .any(|choice| choice.finish_reason == Some(reason))
    }

    fn emitted_tool_call_count(
        output: &[Annotated<NvCreateChatCompletionStreamResponse>],
    ) -> usize {
        output
            .iter()
            .flat_map(|response| response.data.iter())
            .flat_map(|data| data.inner.choices.iter())
            .filter_map(|choice| choice.delta.tool_calls.as_ref())
            .map(|tool_calls| tool_calls.len())
            .sum()
    }

    fn stream_content(output: &[Annotated<NvCreateChatCompletionStreamResponse>]) -> String {
        output
            .iter()
            .flat_map(|response| response.data.iter())
            .flat_map(|data| data.inner.choices.iter())
            .filter_map(|choice| match &choice.delta.content {
                Some(ChatCompletionMessageContent::Text(content)) => Some(content.as_str()),
                _ => None,
            })
            .collect()
    }

    fn assert_glm47_streaming_length_output(
        output: &[Annotated<NvCreateChatCompletionStreamResponse>],
        expected_content: &str,
        split: usize,
    ) {
        assert_eq!(
            stream_content(output),
            expected_content,
            "split at byte {split} must reconstruct the complete visible response"
        );
        assert!(
            output
                .iter()
                .flat_map(|response| response.data.iter())
                .flat_map(|data| data.inner.choices.iter())
                .any(|choice| choice.finish_reason == Some(FinishReason::Length)),
            "split at byte {split} must preserve the terminal length delta"
        );
    }

    #[tokio::test]
    async fn glm47_streaming_length_preserves_prose_and_suppresses_incomplete_marker() {
        let output = apply_glm47_streaming_length(&[
            "I can help. <tool_call>get_weather<arg_key>city</arg_key><arg_value>Par",
        ])
        .await;

        assert_eq!(stream_content(&output), "I can help. ");
        assert!(
            output
                .iter()
                .flat_map(|response| response.data.iter())
                .flat_map(|data| data.inner.choices.iter())
                .any(|choice| choice.finish_reason == Some(FinishReason::Length))
        );
    }

    #[tokio::test]
    async fn glm47_streaming_length_preserves_quoted_marker_prose() {
        let content = r#"The literal "<tool_call>" marker is part of the explanation."#;
        for split in content.char_indices().map(|(index, _)| index).skip(1) {
            let output =
                apply_glm47_streaming_length(&[&content[..split], &content[split..]]).await;
            assert_glm47_streaming_length_output(&output, content, split);
        }
    }

    #[tokio::test]
    async fn glm47_streaming_length_suppresses_incomplete_marker_at_every_split() {
        let input = "I can help. <tool_call>get_weather<arg_key>city</arg_key><arg_value>Par";
        for split in input.char_indices().map(|(index, _)| index).skip(1) {
            let output = apply_glm47_streaming_length(&[&input[..split], &input[split..]]).await;
            assert_glm47_streaming_length_output(&output, "I can help. ", split);
        }
    }

    #[tokio::test]
    async fn glm47_streaming_stop_after_bare_tool_call_marker_reports_length() {
        let output = apply_glm47_streaming_with_terminal(
            &["I'll check. ", "<tool_call>"],
            FinishReason::Stop,
        )
        .await;

        assert_eq!(stream_content(&output), "I'll check. ");
        assert!(has_finish_reason(&output, FinishReason::Length));
        assert!(!has_finish_reason(&output, FinishReason::Stop));
        assert_eq!(emitted_tool_call_count(&output), 0);
    }

    #[tokio::test]
    async fn glm47_streaming_stop_after_partial_function_name_reports_length() {
        let output = apply_glm47_streaming_with_terminal(
            &["I'll check. <tool_call>ipy"],
            FinishReason::Stop,
        )
        .await;

        assert_eq!(stream_content(&output), "I'll check. ");
        assert!(has_finish_reason(&output, FinishReason::Length));
        assert!(!has_finish_reason(&output, FinishReason::Stop));
    }

    #[tokio::test]
    async fn glm47_streaming_stop_after_partial_arguments_reports_length_without_markup() {
        let output = apply_glm47_streaming_with_terminal(
            &["<tool_call>get_weather<arg_key>city</arg_key><arg_value>Par"],
            FinishReason::Stop,
        )
        .await;

        assert!(stream_content(&output).is_empty());
        assert!(has_finish_reason(&output, FinishReason::Length));
        assert!(!has_finish_reason(&output, FinishReason::Stop));
        assert_eq!(emitted_tool_call_count(&output), 0);
    }

    #[tokio::test]
    async fn glm47_streaming_stop_without_marker_keeps_stop() {
        let output = apply_glm47_streaming_with_terminal(
            &["Done. ", "The worker exited normally."],
            FinishReason::Stop,
        )
        .await;

        assert_eq!(stream_content(&output), "Done. The worker exited normally.");
        assert!(has_finish_reason(&output, FinishReason::Stop));
        assert!(!has_finish_reason(&output, FinishReason::Length));
    }

    #[tokio::test]
    async fn glm47_streaming_stop_with_quoted_marker_keeps_stop() {
        let content = r#"The literal "<tool_call>" marker is part of the explanation."#;
        let output = apply_glm47_streaming_with_terminal(&[content], FinishReason::Stop).await;

        assert!(has_finish_reason(&output, FinishReason::Stop));
        assert!(!has_finish_reason(&output, FinishReason::Length));
        assert_eq!(emitted_tool_call_count(&output), 0);
    }

    #[tokio::test]
    async fn glm47_streaming_complete_call_on_stop_keeps_tool_calls() {
        let output = apply_glm47_streaming_with_terminal(
            &["<tool_call>get_weather<arg_key>city</arg_key><arg_value>Paris</arg_value></tool_call>"],
            FinishReason::Stop,
        )
        .await;

        assert_eq!(emitted_tool_call_count(&output), 1);
        assert!(has_finish_reason(&output, FinishReason::ToolCalls));
        assert!(!has_finish_reason(&output, FinishReason::Length));
    }

    /// A prose-only answer never completes a call, so nothing drains the recovery
    /// buffer through the marker path. The buffer must still stay proportional to the
    /// unemitted tail, or every chunk rescans the whole response.
    #[tokio::test]
    async fn glm47_streaming_prose_only_round_trips_without_retaining_the_response() {
        let chunk_text = "the quick brown fox ";
        let chunks: Vec<&str> = std::iter::repeat_n(chunk_text, 400).collect();
        let output = apply_glm47_streaming_length(&chunks).await;

        assert_eq!(stream_content(&output), chunk_text.repeat(400));
    }

    /// The recovery buffer holds only what the client has not seen yet, so a long
    /// prose answer must still arrive whole. Without the compaction this text is
    /// retained and rescanned in full on every chunk.
    #[tokio::test]
    async fn glm47_streaming_long_prose_survives_buffer_compaction() {
        let chunks: Vec<String> = (0..400).map(|i| format!("chunk {i} of prose. ")).collect();
        let expected: String = chunks.concat();
        let borrowed: Vec<&str> = chunks.iter().map(String::as_str).collect();

        let output = apply_glm47_streaming_length(&borrowed).await;

        assert_eq!(stream_content(&output), expected);
    }

    #[tokio::test]
    async fn glm47_streaming_length_keeps_an_empty_safe_terminal_recovery_delta() {
        let output = apply_glm47_streaming_length(&[
            "<tool_call>get_weather<arg_key>city</arg_key><arg_value>Par",
        ])
        .await;

        assert!(stream_content(&output).is_empty());
        let terminal_choices: Vec<_> = output
            .iter()
            .flat_map(|response| response.data.iter())
            .flat_map(|data| data.inner.choices.iter())
            .filter(|choice| choice.finish_reason == Some(FinishReason::Length))
            .collect();
        assert_eq!(
            terminal_choices.len(),
            1,
            "the recovery event must be observable"
        );
        assert!(
            terminal_choices[0].delta.content.is_none()
                && terminal_choices[0].delta.tool_calls.is_none(),
            "the observable recovery delta must not expose raw markup or partial arguments"
        );
    }

    #[test]
    fn effective_tool_predicates_match_protocol_definition() {
        let cases = [
            (
                "no tools",
                serde_json::json!({
                    "model": "test-model",
                    "messages": [{"role": "user", "content": "test"}]
                }),
            ),
            (
                "empty top-level tools",
                serde_json::json!({
                    "model": "test-model",
                    "messages": [{"role": "user", "content": "test"}],
                    "tools": []
                }),
            ),
            (
                "top-level tools",
                serde_json::json!({
                    "model": "test-model",
                    "messages": [{"role": "user", "content": "test"}],
                    "tools": [{
                        "type": "function",
                        "function": {
                            "name": "lookup",
                            "parameters": {"type": "object"}
                        }
                    }]
                }),
            ),
            (
                "empty system tools",
                serde_json::json!({
                    "model": "test-model",
                    "messages": [
                        {"role": "system", "content": "", "tools": []},
                        {"role": "user", "content": "test"}
                    ]
                }),
            ),
            (
                "system tools",
                serde_json::json!({
                    "model": "test-model",
                    "messages": [
                        {"role": "system", "content": "", "tools": [{"name": "lookup"}]},
                        {"role": "user", "content": "test"}
                    ]
                }),
            ),
            (
                "later system tools",
                serde_json::json!({
                    "model": "test-model",
                    "messages": [
                        {"role": "system", "content": "", "tools": []},
                        {"role": "user", "content": "test"},
                        {"role": "system", "content": "", "tools": [{"name": "lookup"}]}
                    ]
                }),
            ),
        ];

        for (case, value) in cases {
            let request: NvCreateChatCompletionRequest =
                serde_json::from_value(value).expect("request must deserialize");
            assert_eq!(
                OpenAIPreprocessor::request_has_effective_tools(&request),
                request.inner.has_effective_tools(),
                "effective-tool predicates diverged for {case}"
            );
        }

        let developer_tools =
            serde_json::from_value::<NvCreateChatCompletionRequest>(serde_json::json!({
                "model": "test-model",
                "messages": [
                    {"role": "developer", "content": "policy", "tools": [{"name": "lookup"}]},
                    {"role": "user", "content": "test"}
                ]
            }));
        match developer_tools {
            Ok(request) => assert_eq!(
                OpenAIPreprocessor::request_has_effective_tools(&request),
                request.inner.has_effective_tools(),
                "generic predicate must track newly supported developer tools"
            ),
            Err(error) => assert!(
                error
                    .to_string()
                    .contains("`tools` is only accepted on system messages, not on role developer"),
                "unexpected developer-tools rejection: {error}"
            ),
        }
    }

    const HARMONY_CALL_TOKEN_ID: TokenIdType = 42;

    struct HiddenStopTokenizer;

    impl crate::tokenizers::traits::Encoder for HiddenStopTokenizer {
        fn encode(&self, input: &str) -> anyhow::Result<Encoding> {
            if input != "<|call|>" {
                anyhow::bail!("unexpected Harmony tool-call end token {input:?}");
            }
            Ok(Encoding::Sp(vec![HARMONY_CALL_TOKEN_ID]))
        }

        fn encode_batch(&self, inputs: &[&str]) -> anyhow::Result<Vec<Encoding>> {
            inputs.iter().map(|input| self.encode(input)).collect()
        }
    }

    impl crate::tokenizers::traits::Decoder for HiddenStopTokenizer {
        fn decode(
            &self,
            _token_ids: &[TokenIdType],
            _skip_special_tokens: bool,
        ) -> anyhow::Result<crate::tokenizers::traits::DecodeResult> {
            Ok(crate::tokenizers::traits::DecodeResult::Complete(
                String::new(),
            ))
        }
    }

    impl Tokenizer for HiddenStopTokenizer {}

    fn hidden_stop_preprocessor() -> OpenAIPreprocessor {
        let mdc = ModelDeploymentCard::load_from_disk(
            "tests/data/sample-models/mock-llama-3.1-8b-instruct",
            None,
        )
        .unwrap();
        let mut preprocessor = match Arc::try_unwrap(OpenAIPreprocessor::new(mdc).unwrap()) {
            Ok(preprocessor) => preprocessor,
            Err(_) => panic!("test preprocessor unexpectedly shared"),
        };
        preprocessor.tool_call_parser = Some("harmony".to_string());
        preprocessor.tokenizer = Arc::new(HiddenStopTokenizer);
        preprocessor
    }

    #[test]
    fn hidden_stop_path_recognizes_dynamic_system_tools_and_tool_choice_none() {
        let dynamic: NvCreateChatCompletionRequest = serde_json::from_value(serde_json::json!({
            "model": "moonshotai/Kimi-K3",
            "messages": [
                {"role": "system", "content": "", "tools": [{"name": "lookup"}]},
                {"role": "user", "content": "test"}
            ]
        }))
        .unwrap();
        let none: NvCreateChatCompletionRequest = serde_json::from_value(serde_json::json!({
            "model": "moonshotai/Kimi-K3",
            "messages": [
                {"role": "system", "content": "", "tools": [{"name": "lookup"}]},
                {"role": "user", "content": "test"}
            ],
            "tool_choice": "none"
        }))
        .unwrap();
        assert!(dynamic.inner.tools.is_none());
        assert!(none.inner.tools.is_none());

        let preprocessor = hidden_stop_preprocessor();
        let mut dynamic_hidden = vec![HARMONY_CALL_TOKEN_ID, 7];
        let dynamic_visible = preprocessor
            .remove_tool_parser_end_tokens_from_hidden_stops(&dynamic, &mut dynamic_hidden)
            .unwrap();
        assert_eq!(dynamic_visible, [HARMONY_CALL_TOKEN_ID]);
        assert_eq!(dynamic_hidden, [7]);

        let mut none_hidden = vec![HARMONY_CALL_TOKEN_ID, 7];
        let none_visible = preprocessor
            .remove_tool_parser_end_tokens_from_hidden_stops(&none, &mut none_hidden)
            .unwrap();
        assert!(none_visible.is_empty());
        assert_eq!(none_hidden, [HARMONY_CALL_TOKEN_ID, 7]);
    }

    async fn apply_kimi_k3_no_tools(
        leaked_reasoning: &str,
    ) -> Vec<Annotated<NvCreateChatCompletionStreamResponse>> {
        let request: NvCreateChatCompletionRequest = serde_json::from_value(serde_json::json!({
            "model": "moonshotai/Kimi-K3",
            "messages": [{"role": "user", "content": "test"}]
        }))
        .unwrap();
        let tool_call_parsing_enabled = OpenAIPreprocessor::tool_call_parsing_enabled(&request);
        assert!(!tool_call_parsing_enabled, "request has no tools");

        let jailed = OpenAIPreprocessor::apply_tool_calling_jail(
            Some("kimi_k3".to_string()),
            request.inner.tool_choice.clone(),
            None,
            false,
            // No tools and no forced choice, so no JSON grammar was installed.
            false,
            stream::iter(vec![
                kimi_k3_reasoning_chunk(leaked_reasoning),
                terminal_chat_stream_chunk(),
            ]),
        );

        OpenAIPreprocessor::apply_tool_call_response_policy(jailed, tool_call_parsing_enabled)
            .collect()
            .await
    }

    async fn apply_kimi_k3_dynamic_tools(
        leaked_reasoning: &str,
    ) -> Vec<Annotated<NvCreateChatCompletionStreamResponse>> {
        let request: NvCreateChatCompletionRequest = serde_json::from_value(serde_json::json!({
            "model": "moonshotai/Kimi-K3",
            "messages": [
                {
                    "role": "system",
                    "content": "",
                    "tools": [{
                        "name": "lookup",
                        "parameters": {
                            "type": "object",
                            "properties": {"query": {"type": "string"}},
                            "required": ["query"]
                        }
                    }]
                },
                {"role": "user", "content": "test"}
            ]
        }))
        .unwrap();
        let tool_call_parsing_enabled = OpenAIPreprocessor::tool_call_parsing_enabled(&request);
        assert!(
            tool_call_parsing_enabled,
            "dynamic system tools grant tool-call response permission"
        );
        let tool_definitions =
            crate::preprocessor::tool_choice::effective_tool_definitions(&request.inner).unwrap();

        let jailed = OpenAIPreprocessor::apply_tool_calling_jail(
            Some("kimi_k3".to_string()),
            request.inner.tool_choice.clone(),
            Some(tool_definitions),
            false,
            false,
            stream::iter(vec![
                kimi_k3_reasoning_chunk(leaked_reasoning),
                terminal_chat_stream_chunk(),
            ]),
        );

        OpenAIPreprocessor::apply_tool_call_response_policy(jailed, tool_call_parsing_enabled)
            .collect()
            .await
    }

    #[tokio::test]
    async fn test_kimi_k3_no_tools_preserves_decoded_content_and_reasoning() {
        let responses = apply_kimi_k3_no_tools(concat!(
            "The user requested an exact integer.",
            "<|open|>response<|sep|>",
            "323",
            "<|close|>response<|sep|>",
            "<|close|>message<|sep|>",
            "<|end_of_msg|>"
        ))
        .await;

        let choices: Vec<_> = responses
            .iter()
            .flat_map(|response| response.data.iter())
            .flat_map(|data| data.inner.choices.iter())
            .collect();
        let content: String = choices
            .iter()
            .filter_map(|choice| match &choice.delta.content {
                Some(ChatCompletionMessageContent::Text(text)) => Some(text.as_str()),
                _ => None,
            })
            .collect();
        let reasoning: String = choices
            .iter()
            .filter_map(|choice| choice.delta.reasoning_content.as_deref())
            .collect();

        assert_eq!(content, "323");
        assert_eq!(reasoning, "The user requested an exact integer.");
        assert!(!content.contains("<|"));
        assert!(!reasoning.contains("<|"));
    }

    #[tokio::test]
    async fn test_kimi_k3_no_tools_suppresses_structured_calls_for_stream_and_batch() {
        let responses = apply_kimi_k3_no_tools(concat!(
            "Use the calculator.",
            "<|open|>tools<|sep|>",
            "<|open|>call tool=\"calc\" index=\"1\"<|sep|>",
            "<|open|>argument key=\"x\" type=\"number\"<|sep|>323",
            "<|close|>argument<|sep|>",
            "<|close|>call<|sep|>",
            "<|close|>tools<|sep|>",
            "<|close|>message<|sep|>",
            "<|end_of_msg|>"
        ))
        .await;

        let choices: Vec<_> = responses
            .iter()
            .flat_map(|response| response.data.iter())
            .flat_map(|data| data.inner.choices.iter())
            .collect();
        assert!(
            choices
                .iter()
                .all(|choice| choice.delta.tool_calls.is_none()),
            "streaming output must not expose parser-produced tool calls"
        );
        assert!(
            choices
                .iter()
                .all(|choice| choice.finish_reason != Some(FinishReason::ToolCalls))
        );
        assert!(
            choices
                .iter()
                .any(|choice| choice.finish_reason == Some(FinishReason::Stop))
        );
        let content = choices
            .iter()
            .filter_map(|choice| match &choice.delta.content {
                Some(ChatCompletionMessageContent::Text(text)) => Some(text.as_str()),
                _ => None,
            })
            .collect::<String>();
        assert!(
            content.is_empty(),
            "a suppressed tool-only turn deliberately fails closed instead of exposing raw XTML"
        );
        assert_eq!(
            choices
                .iter()
                .filter_map(|choice| choice.delta.reasoning_content.as_deref())
                .collect::<String>(),
            "Use the calculator."
        );

        let response =
            crate::protocols::openai::chat_completions::aggregator::DeltaAggregator::apply(
                stream::iter(responses),
                crate::protocols::openai::ParsingOptions::new(None, None),
            )
            .await
            .unwrap();
        let choice = &response.inner.choices[0];
        assert!(
            choice.message.content.as_ref().is_none_or(|content| {
                matches!(content, ChatCompletionMessageContent::Text(text) if text.is_empty())
            }),
            "batch output must not reconstruct the suppressed call as assistant content"
        );
        assert!(choice.message.tool_calls.is_none());
        assert_eq!(choice.finish_reason, Some(FinishReason::Stop));
        assert_eq!(
            choice.message.reasoning_content.as_deref(),
            Some("Use the calculator.")
        );
    }

    #[tokio::test]
    async fn test_kimi_k3_dynamic_only_tools_survive_stream_and_batch_response_policy() {
        let responses = apply_kimi_k3_dynamic_tools(concat!(
            "Use the dynamic lookup tool.",
            "<|open|>tools<|sep|>",
            "<|open|>call tool=\"lookup\" index=\"1\"<|sep|>",
            "<|open|>argument key=\"query\" type=\"string\"<|sep|>weather",
            "<|close|>argument<|sep|>",
            "<|close|>call<|sep|>",
            "<|close|>tools<|sep|>",
            "<|close|>message<|sep|>",
            "<|end_of_msg|>"
        ))
        .await;

        let choices: Vec<_> = responses
            .iter()
            .flat_map(|response| response.data.iter())
            .flat_map(|data| data.inner.choices.iter())
            .collect();
        assert!(
            choices
                .iter()
                .any(|choice| choice.delta.tool_calls.is_some()),
            "streaming output must retain a dynamically declared tool call"
        );
        assert!(
            choices
                .iter()
                .any(|choice| choice.finish_reason == Some(FinishReason::ToolCalls)),
            "streaming finish reason must remain tool_calls"
        );

        let response =
            crate::protocols::openai::chat_completions::aggregator::DeltaAggregator::apply(
                stream::iter(responses),
                crate::protocols::openai::ParsingOptions::new(None, None),
            )
            .await
            .unwrap();
        let choice = &response.inner.choices[0];
        let calls = choice
            .message
            .tool_calls
            .as_ref()
            .expect("batch output must retain the dynamic call");
        assert_eq!(calls.len(), 1);
        assert_eq!(calls[0].function.name, "lookup");
        assert_eq!(choice.finish_reason, Some(FinishReason::ToolCalls));
    }

    #[test]
    fn prompt_invalid_request_maps_to_invalid_argument() {
        let error = PromptRenderError::invalid_request("unsupported model parameter").into();
        let mapped = OpenAIPreprocessor::map_prompt_render_error(error);
        let mapped = mapped
            .downcast_ref::<DynamoError>()
            .expect("prompt validation should map to a DynamoError");

        assert!(matches!(mapped.error_type(), ErrorType::InvalidArgument));
        assert_eq!(mapped.message(), "unsupported model parameter");
    }

    #[test]
    fn ordinary_prompt_error_maps_to_invalid_argument() {
        let mapped = OpenAIPreprocessor::map_prompt_render_error(anyhow::anyhow!(
            "template configuration failed"
        ));
        let mapped = mapped
            .downcast_ref::<DynamoError>()
            .expect("any prompt render failure should map to a DynamoError");

        assert!(matches!(mapped.error_type(), ErrorType::InvalidArgument));
        assert_eq!(mapped.message(), "template configuration failed");
    }

    fn url_entry(u: &str) -> MultimodalData {
        MultimodalData::Url(url::Url::parse(u).unwrap())
    }

    fn preprocessed_with_media(media: Option<MultimodalDataMap>) -> PreprocessedRequest {
        let mut b = PreprocessedRequest::builder();
        b.model("m".to_string())
            .token_ids(vec![1, 2, 3])
            .stop_conditions(StopConditions::default())
            .sampling_options(SamplingOptions::default())
            .output_options(OutputOptions::default());
        if let Some(m) = media {
            b.multi_modal_data(Some(m));
        }
        b.build().unwrap()
    }

    #[test]
    fn test_multimodal_counts_from_preprocessed_mixed() {
        // 2 images, 1 video, 0 audio -> counts reflect vec lengths per modality.
        let mut map: MultimodalDataMap = HashMap::new();
        map.insert(
            "image_url".to_string(),
            vec![url_entry("http://x/a.png"), url_entry("http://x/b.png")],
        );
        map.insert("video_url".to_string(), vec![url_entry("http://x/c.mp4")]);

        let req = preprocessed_with_media(Some(map));
        let counts = MultimodalCounts::from_preprocessed(&req);
        assert_eq!(counts.image, 2);
        assert_eq!(counts.video, 1);
        assert_eq!(counts.audio, 0);
    }

    #[test]
    fn test_multimodal_counts_from_preprocessed_text_only() {
        // No multi_modal_data -> all zero.
        let req = preprocessed_with_media(None);
        let counts = MultimodalCounts::from_preprocessed(&req);
        assert_eq!(counts.image, 0);
        assert_eq!(counts.video, 0);
        assert_eq!(counts.audio, 0);
    }

    #[test]
    fn tool_message_exposes_multimodal_content_parts() {
        let message: ChatCompletionRequestMessage = serde_json::from_value(serde_json::json!({
            "role": "tool",
            "tool_call_id": "call_media",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "data:image/png;base64,aGVsbG8="
                    }
                },
                {
                    "type": "video_url",
                    "video_url": {
                        "url": "https://example.com/clip.mp4"
                    }
                },
                {
                    "type": "audio_url",
                    "audio_url": {
                        "url": "https://example.com/audio.wav"
                    }
                }
            ]
        }))
        .unwrap();

        let parts: Vec<_> = multimodal_content_parts(&message).unwrap().collect();
        assert!(matches!(
            parts[0].as_user().as_ref(),
            ChatCompletionRequestUserMessageContentPart::ImageUrl(_)
        ));
        assert!(matches!(
            parts[1].as_user().as_ref(),
            ChatCompletionRequestUserMessageContentPart::VideoUrl(_)
        ));
        assert!(matches!(
            parts[2].as_user().as_ref(),
            ChatCompletionRequestUserMessageContentPart::AudioUrl(_)
        ));
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn image_token_aggregate_requires_complete_trustworthy_counts() {
        assert_eq!(aggregate_image_tokens(Some(300), 2, 2, false), Some(300));
        assert_eq!(aggregate_image_tokens(Some(300), 2, 3, false), None);
        assert_eq!(aggregate_image_tokens(Some(300), 2, 2, true), None);
        assert_eq!(aggregate_image_tokens(None, 2, 2, false), None);
        assert_eq!(aggregate_image_tokens(Some(0), 0, 0, false), None);
        assert_eq!(
            checked_add_image_tokens(Some(usize::MAX), 1),
            None,
            "overflow must omit the aggregate rather than wrap"
        );
    }

    #[cfg(feature = "mm-routing")]
    struct RoutingTestTokenizer {
        atomic_controls: bool,
        fail_plain_text: bool,
    }

    #[cfg(feature = "mm-routing")]
    impl crate::tokenizers::traits::Encoder for RoutingTestTokenizer {
        fn encode(&self, input: &str) -> anyhow::Result<Encoding> {
            let ids = match input {
                "<image>" => vec![18],
                "<img>" => vec![19],
                "</img>" => vec![20],
                _ => input.bytes().map(|byte| 1000 + u32::from(byte)).collect(),
            };
            Ok(Encoding::Sp(ids))
        }

        fn encode_batch(&self, inputs: &[&str]) -> anyhow::Result<Vec<Encoding>> {
            inputs.iter().map(|input| self.encode(input)).collect()
        }

        fn encode_segments(
            &self,
            segments: &[crate::tokenizers::EncodeSegment<'_>],
        ) -> anyhow::Result<Encoding> {
            let mut ids = Vec::new();
            for segment in segments {
                if segment.allow_special {
                    if !self.atomic_controls {
                        ids.extend([9000, 9001]);
                        continue;
                    }
                    ids.push(match segment.text {
                        "<|media_begin|>" => 163602,
                        "<|media_content|>" => 163603,
                        "<|media_end|>" => 163604,
                        "<img>" => 19,
                        "<image>" => 18,
                        "</img>" => 20,
                        other => anyhow::bail!("unexpected control segment {other:?}"),
                    });
                } else {
                    if self.fail_plain_text {
                        anyhow::bail!("injected plain-text routing encode failure");
                    }
                    ids.extend(segment.text.bytes().map(|byte| 1000 + u32::from(byte)));
                }
            }
            Ok(Encoding::Sp(ids))
        }
    }

    #[cfg(feature = "mm-routing")]
    impl crate::tokenizers::traits::Decoder for RoutingTestTokenizer {
        fn decode(
            &self,
            _token_ids: &[TokenIdType],
            _skip_special_tokens: bool,
        ) -> anyhow::Result<crate::tokenizers::traits::DecodeResult> {
            Ok(crate::tokenizers::traits::DecodeResult::Complete(
                String::new(),
            ))
        }
    }

    #[cfg(feature = "mm-routing")]
    impl Tokenizer for RoutingTestTokenizer {}

    #[cfg(feature = "mm-routing")]
    struct ReferenceK3Tokenizer;

    #[cfg(feature = "mm-routing")]
    impl llm_tokenizer::traits::Encoder for ReferenceK3Tokenizer {
        fn encode(
            &self,
            input: &str,
            _add_special_tokens: bool,
        ) -> anyhow::Result<llm_tokenizer::Encoding> {
            Ok(llm_tokenizer::Encoding::Plain(
                input.bytes().map(|byte| 1000 + u32::from(byte)).collect(),
            ))
        }

        fn encode_batch(
            &self,
            inputs: &[&str],
            add_special_tokens: bool,
        ) -> anyhow::Result<Vec<llm_tokenizer::Encoding>> {
            inputs
                .iter()
                .map(|input| self.encode(input, add_special_tokens))
                .collect()
        }
    }

    #[cfg(feature = "mm-routing")]
    impl llm_tokenizer::traits::Decoder for ReferenceK3Tokenizer {
        fn decode(&self, _token_ids: &[u32], _skip_special_tokens: bool) -> anyhow::Result<String> {
            Ok(String::new())
        }
    }

    #[cfg(feature = "mm-routing")]
    impl llm_tokenizer::traits::Tokenizer for ReferenceK3Tokenizer {
        fn vocab_size(&self) -> usize {
            0
        }

        fn get_special_tokens(&self) -> &llm_tokenizer::SpecialTokens {
            static EMPTY: std::sync::LazyLock<llm_tokenizer::SpecialTokens> =
                std::sync::LazyLock::new(llm_tokenizer::SpecialTokens::default);
            &EMPTY
        }

        fn token_to_id(&self, token: &str) -> Option<u32> {
            match token {
                "<|media_begin|>" => Some(163602),
                "<|media_content|>" => Some(163603),
                "<|media_end|>" => Some(163604),
                "<|media_pad|>" => Some(163605),
                _ => None,
            }
        }

        fn id_to_token(&self, _id: u32) -> Option<String> {
            None
        }

        fn as_any(&self) -> &dyn std::any::Any {
            self
        }
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn kimi_k3_routing_replacement_includes_dimensions_and_structural_tokens() {
        let tokenizer = RoutingTestTokenizer {
            atomic_controls: true,
            fail_plain_text: false,
        };
        let layout = resolve_routing_image_prompt_layout(
            &tokenizer,
            mm_routing::image::ImagePromptKind::KimiK3,
            None,
        )
        .unwrap();
        let image = MmImageEntry {
            mm_hash: 0x1234,
            width: 320,
            height: 240,
        };
        let fill = dynamo_kv_router::protocols::pad_value_for_mm_hash(image.mm_hash);
        let dimension_ids: Vec<TokenIdType> = "image 320x240"
            .bytes()
            .map(|byte| 1000 + u32::from(byte))
            .collect();
        let mut expanded = vec![7];

        append_mm_routing_replacement(&mut expanded, &tokenizer, layout, image, 3).unwrap();

        let mut expected = vec![7, 163602];
        expected.extend(dimension_ids);
        expected.push(163603);
        expected.extend([fill; 3]);
        expected.push(163604);
        assert_eq!(expanded, expected);
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn kimi_k2_routing_replacement_remains_repeated_pad_only() {
        let tokenizer = RoutingTestTokenizer {
            atomic_controls: true,
            fail_plain_text: false,
        };
        let image = MmImageEntry {
            mm_hash: 0x5678,
            width: 320,
            height: 240,
        };
        let fill = dynamo_kv_router::protocols::pad_value_for_mm_hash(image.mm_hash);
        let mut expanded = vec![7];

        append_mm_routing_replacement(
            &mut expanded,
            &tokenizer,
            RoutingImagePromptLayout::RepeatedPad,
            image,
            3,
        )
        .unwrap();

        assert_eq!(expanded, vec![7, fill, fill, fill]);
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn nemotron_routing_replacement_preserves_image_wrappers() {
        let tokenizer = RoutingTestTokenizer {
            // The HF tokenizer backend does not implement segmented encoding;
            // Nemotron must resolve its fixed added tokens through `encode`.
            atomic_controls: false,
            fail_plain_text: false,
        };
        let layout = resolve_routing_image_prompt_layout(
            &tokenizer,
            mm_routing::image::ImagePromptKind::Nemotron,
            Some(18),
        )
        .unwrap();
        let image = MmImageEntry {
            mm_hash: 0x1234,
            width: 224,
            height: 224,
        };
        let fill = dynamo_kv_router::protocols::pad_value_for_mm_hash(image.mm_hash);
        let mut expanded = vec![7];

        append_mm_routing_replacement(&mut expanded, &tokenizer, layout, image, 3).unwrap();

        assert_eq!(expanded, vec![7, 19, fill, fill, fill, 20]);
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn nemotron_layout_rejects_tokenizer_and_config_id_mismatch() {
        let tokenizer = RoutingTestTokenizer {
            atomic_controls: true,
            fail_plain_text: false,
        };

        let error = resolve_routing_image_prompt_layout(
            &tokenizer,
            mm_routing::image::ImagePromptKind::Nemotron,
            Some(99),
        )
        .unwrap_err();

        assert!(error.to_string().contains("does not match config id"));
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn kimi_k3_layout_resolution_rejects_non_atomic_control_tokens() {
        let tokenizer = RoutingTestTokenizer {
            atomic_controls: false,
            fail_plain_text: false,
        };

        let error = resolve_routing_image_prompt_layout(
            &tokenizer,
            mm_routing::image::ImagePromptKind::KimiK3,
            None,
        )
        .unwrap_err();

        assert!(error.to_string().contains("expected exactly one"));
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn kimi_k3_expansion_reports_reference_exact_prompt_length() {
        let tokenizer = RoutingTestTokenizer {
            atomic_controls: true,
            fail_plain_text: false,
        };
        let layout = resolve_routing_image_prompt_layout(
            &tokenizer,
            mm_routing::image::ImagePromptKind::KimiK3,
            None,
        )
        .unwrap();
        let image = MmImageEntry {
            mm_hash: 0x1234,
            width: 320,
            height: 240,
        };
        let dimension_token_count = "image 320x240".len();
        let image_token_count = 3;

        let (expanded, expanded_prompt_len) = expand_mm_routing_tokens(
            &tokenizer,
            layout,
            None,
            163605,
            &[image],
            &[image_token_count],
            &[7, 163605, 8],
        )
        .unwrap();

        // vLLM's K3 prompt update replaces one placeholder with:
        // begin + dimension text + content + image tokens + end.
        let reference_len = 2 + 3 + dimension_token_count + image_token_count;
        assert_eq!(expanded_prompt_len, reference_len);
        assert_eq!(expanded.len(), reference_len);
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn kimi_k3_exact_prompt_length_matches_upstream_processor_replacement() {
        use llm_multimodal::vision::{PreProcessorConfig, VisionProcessorRegistry};

        let config = serde_json::json!({
            "model_type": "kimi_k3",
            "media_placeholder_token_id": 163605
        });
        let reference_tokenizer = ReferenceK3Tokenizer;
        let metadata = llm_multimodal::ModelMetadata {
            model_id: "moonshotai/Kimi-K3",
            tokenizer: &reference_tokenizer,
            config: &config,
        };
        let registry = llm_multimodal::ModelRegistry::new();
        let spec = registry.lookup(&metadata).unwrap();
        let vision_registry = VisionProcessorRegistry::with_defaults();
        let processor = vision_registry
            .find("moonshotai/Kimi-K3", Some("kimi_k3"))
            .unwrap();
        let image = image::DynamicImage::new_rgb8(320, 240);
        let processed = processor
            .preprocess(&[image], &PreProcessorConfig::default())
            .unwrap();
        let reference_replacements = spec.prompt_replacements(&metadata, &processed).unwrap();
        let reference_replacement = &reference_replacements[0];

        let tokenizer = RoutingTestTokenizer {
            atomic_controls: true,
            fail_plain_text: false,
        };
        let layout = resolve_routing_image_prompt_layout(
            &tokenizer,
            mm_routing::image::ImagePromptKind::KimiK3,
            None,
        )
        .unwrap();
        let image_entry = MmImageEntry {
            mm_hash: 0x1234,
            width: 320,
            height: 240,
        };
        let (expanded, expanded_prompt_len) = expand_mm_routing_tokens(
            &tokenizer,
            layout,
            None,
            163605,
            &[image_entry],
            &processed.feature_token_counts,
            &[7, 163605, 8],
        )
        .unwrap();

        let reference_prompt_len = 2 + reference_replacement.tokens.len();
        assert_eq!(expanded_prompt_len, reference_prompt_len);
        assert_eq!(expanded.len(), reference_prompt_len);
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn kimi_k3_plain_text_encode_failure_is_routing_only() {
        let tokenizer = RoutingTestTokenizer {
            atomic_controls: true,
            fail_plain_text: true,
        };
        let layout = resolve_routing_image_prompt_layout(
            &tokenizer,
            mm_routing::image::ImagePromptKind::KimiK3,
            None,
        )
        .unwrap();
        let image = MmImageEntry {
            mm_hash: 0x1234,
            width: 320,
            height: 240,
        };

        let result = try_expand_mm_routing_tokens(
            &tokenizer,
            layout,
            None,
            163605,
            &[image],
            &[3],
            &[7, 163605, 8],
            "moonshotai/Kimi-K3",
        );

        assert!(result.is_none());
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn image_expansion_applies_placeholders_in_request_order() {
        let tokenizer = RoutingTestTokenizer {
            atomic_controls: true,
            fail_plain_text: false,
        };
        let images = [
            MmImageEntry {
                mm_hash: 0x1234,
                width: 320,
                height: 240,
            },
            MmImageEntry {
                mm_hash: 0x5678,
                width: 640,
                height: 480,
            },
        ];
        let first_fill = dynamo_kv_router::protocols::pad_value_for_mm_hash(images[0].mm_hash);
        let second_fill = dynamo_kv_router::protocols::pad_value_for_mm_hash(images[1].mm_hash);

        let (expanded, prompt_len) = expand_mm_routing_tokens(
            &tokenizer,
            RoutingImagePromptLayout::RepeatedPad,
            Some(1),
            10,
            &images,
            &[2, 3],
            &[7, 10, 8, 10, 9],
        )
        .unwrap();

        assert_eq!(
            expanded,
            [
                1,
                7,
                first_fill,
                first_fill,
                8,
                second_fill,
                second_fill,
                second_fill,
                9,
            ]
        );
        assert_eq!(prompt_len, expanded.len());
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn exact_routing_rejects_image_placeholder_count_mismatch() {
        let model_dir = tempfile::tempdir().unwrap();
        std::fs::write(model_dir.path().join("preprocessor_config.json"), "{}").unwrap();
        let counter = mm_routing::image::ImageRoutingProcessor::try_new(
            "Qwen/Qwen3-VL-2B-Instruct",
            Some("qwen3_vl"),
            model_dir.path(),
        )
        .unwrap();
        let mdc = ModelDeploymentCard::load_from_disk(
            "tests/data/sample-models/mock-llama-3.1-8b-instruct",
            None,
        )
        .unwrap();
        let mut preprocessor = match Arc::try_unwrap(OpenAIPreprocessor::new(mdc).unwrap()) {
            Ok(preprocessor) => preprocessor,
            Err(_) => panic!("test preprocessor unexpectedly shared"),
        };
        preprocessor.image_token_counter = Some(counter);
        preprocessor.routing_image_token_id = Some(10);
        preprocessor.routing_image_prompt_layout = Some(RoutingImagePromptLayout::RepeatedPad);
        preprocessor.kv_cache_block_size = 16;
        let image = [MmRoutingEntry::Image {
            mm_hash: 0x1234,
            width: 320,
            height: 240,
        }];

        assert!(
            preprocessor
                .build_mm_exact_routing_info(&image, &[7, 8], None)
                .is_none(),
            "missing placeholder must fail closed at the production boundary"
        );
        assert!(
            preprocessor
                .build_mm_exact_routing_info(&image, &[7, 10, 10, 8], None)
                .is_none(),
            "extra placeholder must fail closed at the production boundary"
        );
    }

    #[cfg(feature = "mm-routing")]
    #[tokio::test]
    async fn nemotron_exact_routing_uses_vllm_dummy_text_budget() {
        let model_dir = tempfile::tempdir().unwrap();
        std::fs::write(
            model_dir.path().join("config.json"),
            serde_json::json!({
                "architectures": [mm_routing::nemotron::MODEL_TYPE],
                "model_type": mm_routing::nemotron::MODEL_TYPE,
                "force_image_size": 512,
                "patch_size": 16,
                "downsample_ratio": 0.5,
                "img_context_token": mm_routing::nemotron::IMAGE_CONTEXT,
                "img_context_token_id": 18,
                "img_start_token": mm_routing::nemotron::IMAGE_START,
                "img_end_token": mm_routing::nemotron::IMAGE_END,
                "vision_config": {
                    "args": {
                        "min_num_patches": 1024,
                        "max_num_patches": 13312
                    }
                }
            })
            .to_string(),
        )
        .unwrap();
        std::fs::write(
            model_dir.path().join("preprocessor_config.json"),
            serde_json::json!({
                "image_processor_type": "NemotronH_Nano_Omni_Reasoning_V3ImageProcessor",
                "image_size": 512,
                "patch_size": 16,
                "downsample_ratio": 0.5
            })
            .to_string(),
        )
        .unwrap();
        let counter = mm_routing::image::ImageRoutingProcessor::try_new(
            "nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8",
            Some(mm_routing::nemotron::MODEL_TYPE),
            model_dir.path(),
        )
        .unwrap();
        let image_text_prompt_len = counter
            .context_budget_text_len(
                Arc::new(RoutingTestTokenizer {
                    atomic_controls: true,
                    fail_plain_text: false,
                }),
                Some("before<image>middle<image>after<image>"),
                3,
            )
            .await;
        assert_eq!(image_text_prompt_len, Some(0));
        let mdc = ModelDeploymentCard::load_from_disk(
            "tests/data/sample-models/mock-llama-3.1-8b-instruct",
            None,
        )
        .unwrap();
        let mut preprocessor = match Arc::try_unwrap(OpenAIPreprocessor::new(mdc).unwrap()) {
            Ok(preprocessor) => preprocessor,
            Err(_) => panic!("test preprocessor unexpectedly shared"),
        };
        preprocessor.image_token_counter = Some(counter);
        preprocessor.routing_image_token_id = Some(18);
        preprocessor.routing_image_prompt_layout = Some(
            resolve_routing_image_prompt_layout(
                &RoutingTestTokenizer {
                    atomic_controls: true,
                    fail_plain_text: false,
                },
                mm_routing::image::ImagePromptKind::Nemotron,
                Some(18),
            )
            .unwrap(),
        );
        preprocessor.context_length = 4096;
        preprocessor.kv_cache_block_size = 16;

        let entries = [0x1234, 0x5678, 0x9abc].map(|mm_hash| MmRoutingEntry::Image {
            mm_hash,
            width: 1920,
            height: 1080,
        });
        let routing = preprocessor
            .build_mm_exact_routing_info(
                &entries,
                &[7, 18, 8, 18, 9, 18, 10],
                image_text_prompt_len,
            )
            .expect("Nemotron routing info should be exact");

        // vLLM 0.28 budgets these three images to 1,323 tokens each. Each
        // image also contributes its atomic <img> and </img> delimiters.
        assert_eq!(routing.expanded_prompt_len, 4 + 3 * (1323 + 2));
        assert_eq!(routing.routing_token_ids.len(), 3984);
        assert_eq!(routing.routing_token_ids[0], 7);
        assert_eq!(routing.routing_token_ids[1], 19);
        assert_eq!(routing.routing_token_ids[1325], 20);
        assert_eq!(routing.routing_token_ids[1326], 8);
        assert!(routing.block_mm_infos.is_empty());

        assert!(
            preprocessor
                .build_mm_exact_routing_info(&entries, &[7, 18, 8, 18, 9, 18, 10], None)
                .is_none(),
            "missing rendered-text length must fail closed"
        );
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn mm_processor_override_is_conservative() {
        assert!(!has_mm_processor_override(None));
        assert!(!has_mm_processor_override(Some(&serde_json::Value::Null)));
        assert!(!has_mm_processor_override(Some(&serde_json::json!({}))));
        assert!(has_mm_processor_override(Some(&serde_json::json!([]))));
        assert!(has_mm_processor_override(Some(
            &serde_json::json!({"min_pixels": 64})
        )));
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn processor_kwargs_disable_exact_mm_routing() {
        assert!(exact_mm_routing_preconditions_met(false, 1, 1, false));
        assert!(!exact_mm_routing_preconditions_met(false, 1, 1, true));
        assert!(!exact_mm_routing_preconditions_met(true, 1, 1, false));
        assert!(!exact_mm_routing_preconditions_met(false, 0, 1, false));
    }

    #[cfg(all(feature = "mm-routing", feature = "media-ffmpeg"))]
    #[test]
    fn decoded_video_hash_requires_exact_routing_eligibility() {
        assert!(should_hash_decoded_video(true, false, false, true));
        assert!(!should_hash_decoded_video(false, false, false, true));
        assert!(!should_hash_decoded_video(true, true, false, true));
        assert!(!should_hash_decoded_video(true, false, true, true));
        assert!(!should_hash_decoded_video(true, false, false, false));
    }

    #[cfg(all(
        feature = "mm-routing",
        feature = "media-ffmpeg",
        feature = "testing-nixl"
    ))]
    #[tokio::test]
    async fn adjacent_videos_reach_media_loader_with_hashing_disabled() {
        let video_bytes = include_bytes!("../tests/data/media/240p_10.mp4");
        let mut server = mockito::Server::new_async().await;
        let video_mock = server
            .mock("GET", "/video.mp4")
            .with_status(200)
            .with_header("content-type", "video/mp4")
            .with_body(&video_bytes[..])
            .expect(2)
            .create_async()
            .await;

        let mdc = ModelDeploymentCard::load_from_disk(
            "tests/data/sample-models/mock-llama-3.1-8b-instruct",
            None,
        )
        .unwrap();
        let mut preprocessor = Arc::try_unwrap(OpenAIPreprocessor::new(mdc).unwrap())
            .unwrap_or_else(|_| panic!("test preprocessor unexpectedly shared"));
        let media_decoder: MediaDecoder = serde_json::from_value(serde_json::json!({
            "video": {"num_frames": 2}
        }))
        .unwrap();
        let media_fetcher = MediaFetcher {
            allow_direct_ip: true,
            allow_direct_port: true,
            allow_private_ips: true,
            ..Default::default()
        };
        preprocessor.media_loader = Some(
            MediaLoader::new(media_decoder, Some(media_fetcher))
                .expect("test media loader must initialize"),
        );
        preprocessor.video_routing_processor = Some(mm_routing::VideoRoutingProcessor::test_stub());

        let request: NvCreateChatCompletionRequest = serde_json::from_value(serde_json::json!({
            "model": "test-model",
            "messages": [{
                "role": "user",
                "content": [
                    {
                        "type": "video_url",
                        "video_url": {"url": format!("{}/video.mp4", server.url())}
                    },
                    {
                        "type": "video_url",
                        "video_url": {"url": format!("{}/video.mp4", server.url())}
                    }
                ]
            }],
            "max_tokens": 1
        }))
        .unwrap();
        let mut builder = PreprocessedRequest::builder();
        builder
            .model("test-model".to_string())
            .token_ids(Vec::new())
            .stop_conditions(Default::default())
            .sampling_options(Default::default())
            .output_options(Default::default());

        let (routing_entries, _) = preprocessor
            .gather_multi_modal_data_with_image_tokens(&request, &mut builder, None, &[])
            .await
            .unwrap();
        let preprocessed = builder.build().unwrap();
        let videos = preprocessed
            .multi_modal_data
            .as_ref()
            .and_then(|media| media.get("video_url"))
            .expect("both decoded videos must be forwarded");

        assert_eq!(videos.len(), 2);
        for video in videos {
            let MultimodalData::Decoded(descriptor) = video else {
                panic!("video must reach the media loader and be decoded");
            };
            assert!(
                descriptor.content_hash().is_none(),
                "adjacent videos must reach the media loader with hashing disabled"
            );
        }
        assert!(routing_entries.is_empty());
        assert!(preprocessed.mm_routing_info.is_none());
        video_mock.assert_async().await;
    }

    #[test]
    fn replace_reserved_media_slot_preserves_alignment_and_returns_errors() {
        let mut map = HashMap::from([(
            "image_url".to_string(),
            vec![
                url_entry("http://x/a.png"),
                MultimodalData::UuidOnly("cached-b".to_string()),
                url_entry("http://x/c.png"),
            ],
        )]);

        OpenAIPreprocessor::replace_reserved_media_slot(
            &mut map,
            "image_url",
            2,
            MultimodalData::RawUrl("decoded-c".to_string()),
        )
        .unwrap();

        let images = &map["image_url"];
        assert!(matches!(images[0], MultimodalData::Url(_)));
        assert!(matches!(
            &images[1],
            MultimodalData::UuidOnly(uuid) if uuid == "cached-b"
        ));
        assert!(matches!(
            &images[2],
            MultimodalData::RawUrl(value) if value == "decoded-c"
        ));

        let error = OpenAIPreprocessor::replace_reserved_media_slot(
            &mut map,
            "image_url",
            3,
            MultimodalData::RawUrl("out-of-range".to_string()),
        )
        .expect_err("an out-of-range reserved slot must return an error");
        assert!(error.to_string().contains("image_url[3]"));
    }

    #[test]
    fn routing_priorities_keep_strict_tier_independent() {
        let hints = crate::protocols::common::extensions::AgentHints {
            priority: Some(-3),
            strict_priority: Some(7),
            ..Default::default()
        };

        assert_eq!(
            routing_priorities(Some(&hints)),
            (Some(0.0), Some(7), Some(-3))
        );
        assert_eq!(routing_priorities(None), (None, None, None));
    }

    fn test_llm_metrics_annotation() -> LLMMetricAnnotation {
        LLMMetricAnnotation {
            input_tokens: 10,
            output_tokens: 20,
            chunk_tokens: 3,
            cached_tokens: Some(4),
            image_tokens: Some(512),
            prefill_worker_id: Some(1),
            prefill_dp_rank: Some(2),
            prefill_worker_type: Some("prefill".to_string()),
            decode_worker_id: Some(3),
            decode_dp_rank: Some(4),
            decode_worker_type: Some("decode".to_string()),
            tokenize_latency: Some(std::time::Duration::from_millis(5)),
            detokenize_total_latency: Some(std::time::Duration::from_micros(50)),
            detokenize_count: Some(6),
            ..Default::default()
        }
    }

    #[test]
    fn llm_metrics_from_annotation_recognizes_both_metric_event_tags() {
        // Both the per-chunk `llm_metrics` event and the payload-only `payload_usage`
        // event carry the serialized LLMMetricAnnotation as their comment and must
        // be observed by the metrics collector.
        let base = test_llm_metrics_annotation()
            .to_annotation::<()>()
            .expect("metrics annotation serializes");
        for tag in [ANNOTATION_LLM_METRICS, ANNOTATION_PAYLOAD_USAGE] {
            let tagged = Annotated::<()> {
                id: None,
                data: None,
                event: Some(tag.to_string()),
                comment: base.comment.clone(),
                error: None,
            };
            let metrics = LLMMetricAnnotation::from_annotation(&tagged)
                .expect("metrics annotation parses")
                .unwrap_or_else(|| panic!("metrics recognized for tag {tag}"));
            assert_eq!(metrics.input_tokens, 10);
            assert_eq!(metrics.output_tokens, 20);
            assert_eq!(metrics.image_tokens, Some(512));
            assert_eq!(metrics.detokenize_count, Some(6));
        }
    }

    #[test]
    fn llm_metrics_from_annotation_ignores_untagged_and_other_events() {
        // No event → not metrics (per-chunk metrics are event-tagged again).
        let untagged = Annotated::<()> {
            id: None,
            data: None,
            event: None,
            comment: Some(vec!["{\"input_tokens\":1}".to_string()]),
            error: None,
        };
        assert!(
            LLMMetricAnnotation::from_annotation(&untagged)
                .expect("untagged chunk is not an error")
                .is_none()
        );

        // A different event tag → not metrics.
        let other = Annotated::<()> {
            id: None,
            data: None,
            event: Some(ANNOTATION_TOKEN_IDS.to_string()),
            comment: None,
            error: None,
        };
        assert!(
            LLMMetricAnnotation::from_annotation(&other)
                .expect("other event is not an error")
                .is_none()
        );
    }

    #[test]
    fn special_token_requirements_are_resolved_once_per_model() {
        if crate::test_utils::run_isolated(
            concat!(
                module_path!(),
                "::special_token_requirements_are_resolved_once_per_model"
            ),
            &[(env_llm::DYN_PARSER_VERSION, "2")],
        ) {
            return;
        }
        static CREATED: std::sync::atomic::AtomicUsize = std::sync::atomic::AtomicUsize::new(0);
        dynamo_parsers_v2::register_unified_parser("kimi_k3", |tools| {
            CREATED.fetch_add(1, std::sync::atomic::Ordering::SeqCst);
            dynamo_parsers_v2::create_unified_parser_for_family("gemma4", tools)
        });
        CREATED.store(0, std::sync::atomic::Ordering::SeqCst);
        let mut card = ModelDeploymentCard::load_from_disk(
            "tests/data/sample-models/mock-llama-3.1-8b-instruct",
            None,
        )
        .unwrap();
        card.model_type = crate::model_type::ModelType::Chat;
        card.runtime_config.tool_call_parser = Some("kimi_k3".into());
        let preprocessor = OpenAIPreprocessor::new(card).unwrap();
        assert_eq!(CREATED.load(std::sync::atomic::Ordering::SeqCst), 1);
        for skip in [None, Some(false), Some(true)] {
            let mut request = parser_route_test_request(ChatCompletionToolChoiceOption::Auto);
            request.common.skip_special_tokens = skip;
            for lora in [None, Some("adapter".to_string())] {
                let output = preprocessor
                    .builder_with_lora(&request, lora)
                    .unwrap()
                    .token_ids(vec![])
                    .build()
                    .unwrap();
                assert_eq!(
                    output.output_options.skip_special_tokens,
                    skip.or(Some(false))
                );
                assert_eq!(CREATED.load(std::sync::atomic::Ordering::SeqCst), 1);
            }
        }
        dynamo_parsers_v2::unregister_unified_parser("kimi_k3");
    }

    #[test]
    fn special_token_factory_failure_rejects_model_setup() {
        if crate::test_utils::run_isolated(
            concat!(
                module_path!(),
                "::special_token_factory_failure_rejects_model_setup"
            ),
            &[(env_llm::DYN_PARSER_VERSION, "2")],
        ) {
            return;
        }
        dynamo_parsers_v2::register_unified_parser("kimi_k3", |_| {
            anyhow::bail!("factory unavailable")
        });
        let mut card = ModelDeploymentCard::load_from_disk(
            "tests/data/sample-models/mock-llama-3.1-8b-instruct",
            None,
        )
        .unwrap();
        card.model_type = crate::model_type::ModelType::Chat;
        card.runtime_config.tool_call_parser = Some("kimi_k3".into());
        let error = OpenAIPreprocessor::new(card)
            .err()
            .expect("factory failure must reject model setup");
        assert!(
            error
                .to_string()
                .contains("special-token requirements for kimi_k3")
        );
        assert!(format!("{error:#}").contains("factory unavailable"));
        dynamo_parsers_v2::unregister_unified_parser("kimi_k3");
    }

    /// PRE.1 — `skip_special_tokens` default. See `lib/llm/PREPROCESSOR_CASES.md`.
    #[test]
    fn test_parser_requires_special_tokens() {
        let cases: &[(Option<&str>, Option<&str>, bool, &str)] = &[
            (
                Some("gemma4"),
                None,
                true,
                "gemma4 tool-call only → required",
            ),
            (
                None,
                Some("gemma4"),
                true,
                "gemma4 reasoning only → required",
            ),
            (
                Some("gemma-4"),
                None,
                true,
                "gemma-4 hyphen alias (tool) → required",
            ),
            (
                None,
                Some("gemma-4"),
                true,
                "gemma-4 hyphen alias (reasoning) → required",
            ),
            (
                Some("gemma4"),
                Some("gemma4"),
                true,
                "gemma4 paired → required",
            ),
            (Some("hermes"), None, false, "hermes → not required"),
            (
                Some("harmony"),
                None,
                true,
                "harmony tool-call only → required",
            ),
            (
                None,
                Some("gpt_oss"),
                true,
                "gpt_oss reasoning only → required",
            ),
            (
                Some("harmony"),
                Some("gpt_oss"),
                true,
                "harmony + gpt_oss paired → required",
            ),
            (
                Some("kimi_k2"),
                Some("kimi_k25"),
                true,
                "kimi_k2 + kimi_k25 paired → required \
                 (tool-call markers `<|tool_calls_section_*|>` and reasoning \
                  marker `</think>` are special tokens that get stripped under \
                  the default skip_special_tokens=true)",
            ),
            (
                None,
                Some("kimi_k25"),
                true,
                "kimi_k25 reasoning only → required (`</think>` is special token id 163607)",
            ),
            (
                Some("kimi_k3"),
                Some("kimi_k3"),
                true,
                "kimi_k3 paired XTML parsers → required",
            ),
            (
                Some("kimi-k3"),
                Some("kimi-k3"),
                true,
                "kimi-k3 aliases → required",
            ),
            (
                None,
                Some("mistral"),
                true,
                "mistral reasoning only → required (`[THINK]` / `[/THINK]` are special tokens)",
            ),
            (
                Some("kimi_k2"),
                None,
                true,
                "kimi_k2 tool-call only → required \
                 (`<|tool_calls_section_begin|>` / `<|tool_calls_section_end|>` are special)",
            ),
            (
                Some("minimax_m3"),
                Some("minimax_m3"),
                true,
                "minimax_m3 paired → required",
            ),
            (
                Some("minimax-m3-nom"),
                Some("minimax-m3"),
                true,
                "MiniMax M3 SGLang aliases → required",
            ),
            (
                Some("muse_glimmer"),
                None,
                true,
                "muse_glimmer tool-call only → required \
                 (`<|start|>` / `<|message|>` / `<|eom|>` / `<|eot|>` are special)",
            ),
            (
                None,
                Some("muse_glimmer"),
                true,
                "muse_glimmer reasoning-name only → required",
            ),
            (
                Some("muse"),
                None,
                true,
                "muse (SGLang's registered name, tool) → required",
            ),
            (None, None, false, "no parsers → not required"),
        ];
        for (tool, reasoning, expected, desc) in cases {
            assert_eq!(
                OpenAIPreprocessor::parser_requires_special_tokens(*tool, *reasoning).unwrap(),
                *expected,
                "FAILED: {desc}",
            );
        }
    }

    /// Guard: a caller forcing `skip_special_tokens=true` while a
    /// special-token-dependent parser is active strips the markers before
    /// parsing → silent empty tool_calls / leaked markup. Only that exact
    /// combination should trip the warning condition.
    #[test]
    fn test_special_tokens_will_be_stripped() {
        for skip in [None, Some(false), Some(true)] {
            for required in [false, true] {
                assert_eq!(
                    OpenAIPreprocessor::special_tokens_will_be_stripped(skip, required),
                    skip == Some(true) && required,
                );
            }
        }
    }

    #[test]
    fn test_kimi_k3_jail_always_unwraps_xtml_response_channels() {
        let parser = "kimi_k3".to_string();
        assert!(OpenAIPreprocessor::should_apply_tool_jail(Some(&parser), None, false).unwrap());

        let alias = "kimi-k3".to_string();
        assert!(
            OpenAIPreprocessor::should_apply_tool_jail(
                Some(&alias),
                Some(&ChatCompletionToolChoiceOption::None),
                true,
            )
            .unwrap()
        );
    }

    /// Verifies which force-reasoning parsers use guided-output shape detection.
    #[test]
    fn test_reasoning_before_guided_json_parser_allowlist() {
        for parser in [
            "deepseek_r1",
            "deepseek_v3",
            "deepseek_v3_1",
            "deepseek_v3_2",
            "step3",
            "kimi_k25",
            "mistral",
            "minimax_m2",
            "nemotron_nano",
            "nemotron3",
            "nemotron_v3",
        ] {
            assert!(
                OpenAIPreprocessor::supports_reasoning_before_guided_json(Some(parser)),
                "{parser} should inspect guided output shape"
            );
        }

        assert!(
            !OpenAIPreprocessor::supports_reasoning_before_guided_json(Some(
                "minimax_append_think"
            )),
            "minimax_append_think must retain the guided-JSON bypass"
        );
    }

    /// Verifies parser-specific openers that can be confused with guided JSON.
    #[test]
    fn test_guided_json_reasoning_start_tokens() {
        assert_eq!(
            OpenAIPreprocessor::guided_json_reasoning_start_token(Some("mistral")),
            Some("[THINK]")
        );
        assert_eq!(
            OpenAIPreprocessor::guided_json_reasoning_start_token(Some("nemotron_v3")),
            None
        );
    }

    #[test]
    fn test_prompt_injected_reasoning_start_by_parser() {
        let cases = [
            (
                Some("minimax_m3"),
                Some("...<mm:think>\n"),
                true,
                "MiniMax M3 starts from <mm:think>",
            ),
            (
                Some("minimax-m3"),
                Some("...</mm:think>\n"),
                false,
                "MiniMax M3 prefilled end marker means not in reasoning",
            ),
            (
                Some("minimax_m3"),
                Some("...<think>\n"),
                false,
                "MiniMax M3 must not use generic <think>",
            ),
            (
                Some("qwen3"),
                Some("...<think>\n"),
                true,
                "Qwen-style templated <think> behavior remains",
            ),
            (
                Some("kimi_k3"),
                Some("...<|open|>think<|sep|>\n"),
                true,
                "Kimi K3 canonical XTML prompt retains legacy behavior",
            ),
            (
                Some("kimi-k3"),
                Some("...<|open|> think <|sep|>\n"),
                true,
                "Kimi K3 ignores whitespace inside the suffix",
            ),
            (
                Some("kimi-k3"),
                Some("...<|open|>\u{2003}think\t<|sep|>\n"),
                true,
                "Kimi K3 ignores Unicode whitespace inside the suffix",
            ),
            (
                Some("kimi_k3"),
                Some("think<|sep|>"),
                false,
                "Kimi K3 requires the complete suffix",
            ),
            (
                Some("kimi-k3"),
                Some("...<think>\n"),
                false,
                "Kimi K3 must not use the generic think marker",
            ),
            (
                Some("deepseek_v4"),
                Some("...<think>\n"),
                true,
                "existing <think> parser behavior remains",
            ),
            (
                None,
                Some("...<think>\n"),
                true,
                "legacy no-parser detection",
            ),
            (Some("minimax_m3"), None, false, "no prompt"),
        ];

        for (parser, prompt, expected, desc) in cases {
            assert_eq!(
                OpenAIPreprocessor::prompt_injected_reasoning_start(parser, prompt),
                expected,
                "FAILED: {desc}",
            );
        }
    }

    #[test]
    fn tool_only_unified_family_detects_its_prompt_injected_reasoning_marker() {
        let cases = [
            (
                "gemma4",
                "...<|channel>thought",
                "Gemma 4 tool-only routing uses its thought channel",
            ),
            (
                "kimi_k3",
                "...<|open|>think<|sep|>",
                "Kimi K3 tool-only routing uses its think channel",
            ),
            (
                "muse_glimmer",
                "...assistant to=self<|message|>",
                "Muse tool-only routing uses its thought message",
            ),
        ];

        for (family, prompt, description) in cases {
            assert!(
                OpenAIPreprocessor::prompt_injected_reasoning_start_for_parsers(
                    Some(family),
                    None,
                    Some(prompt),
                ),
                "{description}"
            );
        }

        assert!(
            !OpenAIPreprocessor::prompt_injected_reasoning_start_for_parsers(
                Some("gemma4"),
                Some("qwen3"),
                Some("...<|channel>thought"),
            ),
            "mismatched parser families must keep the configured reasoning parser"
        );
    }

    #[test]
    fn test_prompt_injected_reasoning_ended_backend_arg_by_parser() {
        let cases = [
            (
                Some("minimax_m2"),
                Some("...<think>\n"),
                Some(false),
                "MiniMax M2 needs native backend reasoning state aligned with the prompt",
            ),
            (
                Some("minimax_m3"),
                Some("...<mm:think>\n"),
                Some(false),
                "MiniMax M3 needs native backend reasoning state aligned with the prompt",
            ),
            (
                Some("kimi_k3"),
                Some("...<|open|>think<|sep|>\n"),
                Some(false),
                "Kimi K3 canonical prompt keeps its backend reasoning state",
            ),
            (
                Some("kimi-k3"),
                Some("...<|open|> think <|sep|>\n"),
                Some(false),
                "Kimi K3 spaced header keeps its backend reasoning state",
            ),
            (
                Some("deepseek_v4"),
                Some("...<think>\n"),
                None,
                "DeepSeek V4 native guided JSON must not be forced into reasoning mode",
            ),
            (
                Some("deepseek-v4"),
                Some("...<think>\n"),
                None,
                "DeepSeek V4 alias must not receive reasoning_ended=false",
            ),
            (
                Some("qwen3"),
                Some("...<think>\n"),
                None,
                "Qwen-style prompt injection is handled by Dynamo postprocessing only",
            ),
            (
                Some("minimax_m2"),
                Some("plain prompt"),
                None,
                "no injected reasoning opener means no backend state override",
            ),
        ];

        for (parser, prompt, expected, desc) in cases {
            assert_eq!(
                OpenAIPreprocessor::prompt_injected_reasoning_ended_arg(parser, prompt),
                expected,
                "FAILED: {desc}",
            );
        }
    }

    #[test]
    fn test_backend_extra_args_preserves_nvext_and_sampling_extensions() {
        let request: NvCreateChatCompletionRequest = serde_json::from_value(serde_json::json!({
            "model": "test-model",
            "messages": [{"role": "user", "content": "hi"}],
            "detokenize": false,
            "allowed_token_ids": [10, 11],
            "bad_words_token_ids": [[12, 13]],
            "logprob_token_ids": [14, 15],
            "nvext": {
                "cache_salt": "step_7",
                "extra_fields": ["completion_token_ids"],
                "metadata_upload": {
                    "url": "s3://bucket/root/rollouts"
                }
            }
        }))
        .unwrap();

        let extra_args = OpenAIPreprocessor::backend_extra_args(&request, false, None).unwrap();

        assert_eq!(extra_args["nvext"]["cache_salt"], "step_7");
        assert_eq!(
            extra_args["nvext"]["extra_fields"],
            serde_json::json!(["completion_token_ids"])
        );
        assert_eq!(
            extra_args["nvext"]["metadata_upload"],
            serde_json::json!({
                "url": "s3://bucket/root/rollouts"
            })
        );
        assert_eq!(extra_args["sampling_options"]["detokenize"], false);
        assert_eq!(
            extra_args["sampling_options"]["allowed_token_ids"],
            serde_json::json!([10, 11])
        );
        assert_eq!(
            extra_args["sampling_options"]["bad_words_token_ids"],
            serde_json::json!([[12, 13]])
        );
        assert_eq!(
            extra_args["sampling_options"]["logprob_token_ids"],
            serde_json::json!([14, 15])
        );
    }

    #[test]
    fn capture_prompt_token_ids_is_exact_and_opt_in() {
        let requested: NvCreateChatCompletionRequest = serde_json::from_value(serde_json::json!({
            "model": "test-model",
            "messages": [{"role": "user", "content": "hi"}],
            "nvext": {"extra_fields": ["prompt_token_ids"]}
        }))
        .unwrap();
        let tracker = RequestTracker::new();

        OpenAIPreprocessor::capture_prompt_token_ids(&requested, Some(&tracker), &[101, 102, 103]);
        assert_eq!(tracker.prompt_token_ids(), Some(&[101u32, 102, 103][..]));

        let ordinary: NvCreateChatCompletionRequest = serde_json::from_value(serde_json::json!({
            "model": "test-model",
            "messages": [{"role": "user", "content": "hi"}]
        }))
        .unwrap();
        let ordinary_tracker = RequestTracker::new();
        OpenAIPreprocessor::capture_prompt_token_ids(
            &ordinary,
            Some(&ordinary_tracker),
            &[201, 202],
        );
        assert!(ordinary_tracker.prompt_token_ids().is_none());
    }

    fn chat_request_with_args(
        chat_template_args: Option<HashMap<String, serde_json::Value>>,
    ) -> NvCreateChatCompletionRequest {
        let mut request: NvCreateChatCompletionRequest =
            serde_json::from_value(serde_json::json!({
                "model": "test-model",
                "messages": [{"role": "user", "content": "hi"}]
            }))
            .unwrap();
        request.chat_template_args = chat_template_args;
        request
    }

    fn runtime_config_with_default_thinking_mode(
        mode: &str,
    ) -> crate::local_model::runtime_config::ModelRuntimeConfig {
        let mut runtime_config = crate::local_model::runtime_config::ModelRuntimeConfig::new();
        runtime_config
            .set_engine_specific(DEFAULT_THINKING_MODE_RUNTIME_KEY, mode)
            .unwrap();
        runtime_config
    }

    #[test]
    fn test_default_thinking_mode_disabled_adds_template_args() {
        let runtime_config = runtime_config_with_default_thinking_mode("disabled");
        let mut request = chat_request_with_args(None);

        OpenAIPreprocessor::apply_default_thinking_mode_from_runtime_config(
            &runtime_config,
            &mut request,
        );

        let args = request.chat_template_args.as_ref().unwrap();
        assert_eq!(args.get("thinking"), Some(&serde_json::json!(false)));
        assert_eq!(args.get("enable_thinking"), Some(&serde_json::json!(false)));
        assert_eq!(
            args.get("thinking_mode"),
            Some(&serde_json::json!("disabled"))
        );
    }

    #[test]
    fn test_default_thinking_mode_enabled_adds_template_args() {
        let runtime_config = runtime_config_with_default_thinking_mode("enabled");
        let mut request = chat_request_with_args(None);

        OpenAIPreprocessor::apply_default_thinking_mode_from_runtime_config(
            &runtime_config,
            &mut request,
        );

        let args = request.chat_template_args.as_ref().unwrap();
        assert_eq!(args.get("thinking"), Some(&serde_json::json!(true)));
        assert_eq!(args.get("enable_thinking"), Some(&serde_json::json!(true)));
        assert_eq!(
            args.get("thinking_mode"),
            Some(&serde_json::json!("enabled"))
        );
    }

    #[test]
    fn test_gemma4_default_thinking_mode_controls_reasoning_parser() {
        for (mode, expected_disabled) in [("enabled", false), ("disabled", true)] {
            let runtime_config = runtime_config_with_default_thinking_mode(mode);
            let mut request = chat_request_with_args(None);

            OpenAIPreprocessor::apply_default_thinking_mode_from_runtime_config(
                &runtime_config,
                &mut request,
            );

            for parser in ["gemma4", "gemma-4"] {
                assert_eq!(
                    OpenAIPreprocessor::is_reasoning_disabled_by_request(
                        Some(parser),
                        request.chat_template_args.as_ref(),
                    ),
                    expected_disabled,
                    "parser={parser}, default_thinking_mode={mode}",
                );
            }
        }
    }

    #[test]
    fn test_default_thinking_mode_does_not_override_request() {
        let runtime_config = runtime_config_with_default_thinking_mode("disabled");
        let mut request = chat_request_with_args(Some(HashMap::from([(
            "thinking_mode".to_string(),
            serde_json::json!("enabled"),
        )])));

        OpenAIPreprocessor::apply_default_thinking_mode_from_runtime_config(
            &runtime_config,
            &mut request,
        );

        let args = request.chat_template_args.as_ref().unwrap();
        assert_eq!(
            args.get("thinking_mode"),
            Some(&serde_json::json!("enabled"))
        );
        assert!(!args.contains_key("thinking"));
        assert!(!args.contains_key("enable_thinking"));
    }

    #[test]
    fn test_default_thinking_mode_precedes_parser_implicit_default() {
        let runtime_config = runtime_config_with_default_thinking_mode("disabled");
        let mut request = chat_request_with_args(None);

        OpenAIPreprocessor::apply_default_thinking_mode_from_runtime_config(
            &runtime_config,
            &mut request,
        );
        OpenAIPreprocessor::normalize_thinking_arg(&mut request, Some("kimi_k25"), None);

        let args = request.chat_template_args.as_ref().unwrap();
        assert_eq!(args.get("thinking"), Some(&serde_json::json!(false)));
        assert_eq!(args.get("enable_thinking"), Some(&serde_json::json!(false)));
        assert_eq!(
            args.get("thinking_mode"),
            Some(&serde_json::json!("disabled"))
        );
    }

    #[test]
    fn test_default_thinking_mode_does_not_override_reasoning_effort() {
        let runtime_config = runtime_config_with_default_thinking_mode("disabled");
        let mut request = chat_request_with_args(Some(HashMap::from([(
            "reasoning_effort".to_string(),
            serde_json::json!("high"),
        )])));

        OpenAIPreprocessor::apply_default_thinking_mode_from_runtime_config(
            &runtime_config,
            &mut request,
        );

        let args = request.chat_template_args.as_ref().unwrap();
        assert_eq!(
            args.get("reasoning_effort"),
            Some(&serde_json::json!("high"))
        );
        assert!(!args.contains_key("thinking"));
        assert!(!args.contains_key("enable_thinking"));
        assert!(!args.contains_key("thinking_mode"));
    }

    /// Verifies template reasoning controls are forwarded to a configured parser.
    #[test]
    fn test_backend_extra_args_forwards_reasoning_template_args() {
        for enable_thinking in [true, false] {
            let request: NvCreateChatCompletionRequest =
                serde_json::from_value(serde_json::json!({
                    "model": "test-model",
                    "messages": [{"role": "user", "content": "hi"}],
                    "chat_template_kwargs": {
                        "enable_thinking": enable_thinking,
                        "reasoning_effort": "high"
                    }
                }))
                .unwrap();

            let extra_args =
                OpenAIPreprocessor::backend_extra_args(&request, true, Some(false)).unwrap();

            assert_eq!(
                extra_args["reasoning_parser_kwargs"]["chat_template_kwargs"],
                serde_json::json!({
                    "enable_thinking": enable_thinking,
                    "reasoning_effort": "high"
                })
            );
            assert_eq!(extra_args["reasoning_ended"], false);
        }
    }

    /// Verifies parser metadata is omitted when no reasoning parser is configured.
    #[test]
    fn test_backend_extra_args_omits_reasoning_metadata_without_configured_parser() {
        let request: NvCreateChatCompletionRequest = serde_json::from_value(serde_json::json!({
            "model": "test-model",
            "messages": [{"role": "user", "content": "hi"}],
            "detokenize": false,
            "chat_template_kwargs": {
                "enable_thinking": true
            }
        }))
        .unwrap();

        let extra_args =
            OpenAIPreprocessor::backend_extra_args(&request, false, Some(false)).unwrap();

        assert_eq!(extra_args["sampling_options"]["detokenize"], false);
        assert!(extra_args.get("reasoning_parser_kwargs").is_none());
        assert!(extra_args.get("reasoning_ended").is_none());
    }

    /// Verifies no parser metadata is added when template arguments are absent.
    #[test]
    fn test_backend_extra_args_omits_reasoning_metadata_without_template_args() {
        let request: NvCreateChatCompletionRequest = serde_json::from_value(serde_json::json!({
            "model": "test-model",
            "messages": [{"role": "user", "content": "hi"}]
        }))
        .unwrap();

        assert!(OpenAIPreprocessor::backend_extra_args(&request, true, None).is_none());
    }

    #[test]
    fn test_sglang_gpt_oss_structured_output_rejects_thinking_budget() {
        use crate::local_model::runtime_config::SGLANG_GENERATE_CAPABILITY;

        let mut mdc = ModelDeploymentCard::load_from_disk(
            "tests/data/sample-models/mock-llama-3.1-8b-instruct",
            None,
        )
        .unwrap();
        mdc.runtime_config.reasoning_parser = Some("gpt_oss".to_string());
        mdc.runtime_config
            .set_engine_specific(SGLANG_GENERATE_CAPABILITY, true)
            .unwrap();
        let preprocessor = OpenAIPreprocessor::new(mdc).unwrap();

        for response_format in [
            serde_json::json!({"type": "json_object"}),
            serde_json::json!({"type": "json_schema", "json_schema": {
                "name": "result", "schema": {"type": "object", "properties": {}}
            }}),
        ] {
            for budget_fields in [
                serde_json::json!({"thinking_token_budget": 32}),
                serde_json::json!({"thinking_token_budget": 0}),
                serde_json::json!({"nvext": {"max_thinking_tokens": 16}}),
                serde_json::json!({"thinking_token_budget": 0, "nvext": {"max_thinking_tokens": 16}}),
            ] {
                let mut value = serde_json::json!({
                    "model": "test-model",
                    "messages": [{"role": "user", "content": "hi"}],
                    "response_format": response_format,
                });
                value
                    .as_object_mut()
                    .unwrap()
                    .extend(budget_fields.as_object().unwrap().clone());
                let request: NvCreateChatCompletionRequest = serde_json::from_value(value).unwrap();
                let error = preprocessor
                    .builder(&request)
                    .err()
                    .expect("SGLang cannot honor GPT-OSS structured output with a thinking budget");
                assert_eq!(
                    error.downcast_ref::<DynamoError>().unwrap().error_type(),
                    ErrorType::InvalidArgument,
                );
                assert!(error.to_string().contains("thinking_token_budget"));
            }
        }
    }

    #[test]
    fn test_gpt_oss_thinking_budget_preserves_supported_output_paths() {
        use crate::local_model::runtime_config::{
            SGLANG_GENERATE_CAPABILITY, VLLM_INFERENCE_V1_GENERATE_CAPABILITY,
        };

        for (capability, budget, structured, expected_reasoning) in [
            (SGLANG_GENERATE_CAPABILITY, Some(32), false, true),
            (SGLANG_GENERATE_CAPABILITY, None, true, false),
            (VLLM_INFERENCE_V1_GENERATE_CAPABILITY, Some(0), true, true),
        ] {
            let mut mdc = ModelDeploymentCard::load_from_disk(
                "tests/data/sample-models/mock-llama-3.1-8b-instruct",
                None,
            )
            .unwrap();
            mdc.runtime_config.reasoning_parser = Some("gpt_oss".to_string());
            mdc.runtime_config
                .set_engine_specific(capability, true)
                .unwrap();
            let preprocessor = OpenAIPreprocessor::new(mdc).unwrap();
            let request: NvCreateChatCompletionRequest = serde_json::from_value(serde_json::json!({
                "model": "test-model",
                "messages": [{"role": "user", "content": "hi"}],
                "thinking_token_budget": budget,
                "response_format": if structured { serde_json::json!({"type": "json_object"}) } else { serde_json::Value::Null },
            })).unwrap();
            let preprocessed = preprocessor
                .builder(&request)
                .unwrap()
                .token_ids(vec![1])
                .build()
                .unwrap();
            assert_eq!(preprocessed.require_reasoning, expected_reasoning);
            assert_eq!(preprocessed.stop_conditions.max_thinking_tokens, budget);
        }
    }

    #[test]
    fn test_request_requires_reasoning_with_thinking_budget() {
        let cases = [
            (
                serde_json::json!({"thinking_token_budget": 32}),
                Some("qwen3"),
                true,
            ),
            (
                serde_json::json!({"thinking_token_budget": 0}),
                Some("qwen3"),
                true,
            ),
            (
                serde_json::json!({"nvext": {"max_thinking_tokens": 16}}),
                Some("qwen3"),
                true,
            ),
            (serde_json::json!({}), Some("qwen3"), false),
            (
                serde_json::json!({"thinking_token_budget": 32}),
                None,
                false,
            ),
            (
                serde_json::json!({"thinking_token_budget": 32}),
                Some("gemma4"),
                false,
            ),
            (
                serde_json::json!({"thinking_token_budget": 32, "chat_template_kwargs": {"enable_thinking": true}}),
                Some("gemma4"),
                true,
            ),
            (
                serde_json::json!({"thinking_token_budget": 32, "chat_template_kwargs": {"enable_thinking": false}}),
                Some("qwen3"),
                false,
            ),
            (
                serde_json::json!({"thinking_token_budget": 32, "response_format": {"type": "json_object"}}),
                Some("qwen3"),
                true,
            ),
            (
                serde_json::json!({"response_format": {"type": "json_object"}}),
                Some("qwen3"),
                true,
            ),
            (
                serde_json::json!({"response_format": {"type": "json_object"}}),
                Some("gpt_oss"),
                false,
            ),
        ];
        for (fields, parser, expected) in cases {
            let mut value = serde_json::json!({
                "model": "test-model",
                "messages": [{"role": "user", "content": "hi"}]
            });
            value
                .as_object_mut()
                .unwrap()
                .extend(fields.as_object().unwrap().clone());
            let request: NvCreateChatCompletionRequest = serde_json::from_value(value).unwrap();
            let has_budget = request
                .extract_stop_conditions()
                .unwrap()
                .max_thinking_tokens
                .is_some();
            assert_eq!(
                OpenAIPreprocessor::request_requires_reasoning(&request, parser, has_budget),
                expected,
                "parser={parser:?}, fields={fields}",
            );
        }
    }

    /// Verifies the SGLang reasoning gate covers forced tool JSON and
    /// structured assistant output while honoring per-request thinking controls.
    #[test]
    fn test_guided_output_requires_reasoning() {
        let request = |tool_choice: serde_json::Value, enable_thinking: Option<bool>| {
            let mut value = serde_json::json!({
                "model": "test-model",
                "messages": [{"role": "user", "content": "use the tool"}],
                "tools": [{
                    "type": "function",
                    "function": {
                        "name": "lookup",
                        "parameters": {"type": "object", "properties": {}}
                    }
                }],
                "tool_choice": tool_choice
            });
            if let Some(enabled) = enable_thinking {
                value["chat_template_kwargs"] = serde_json::json!({
                    "enable_thinking": enabled
                });
            }
            serde_json::from_value::<NvCreateChatCompletionRequest>(value).unwrap()
        };

        let required = request(serde_json::json!("required"), Some(true));
        assert!(OpenAIPreprocessor::guided_output_requires_reasoning(
            &required,
            Some("nemotron_v3")
        ));

        let named = request(
            serde_json::json!({
                "type": "function",
                "function": {"name": "lookup"}
            }),
            None,
        );
        assert!(OpenAIPreprocessor::guided_output_requires_reasoning(
            &named,
            Some("nemotron_v3")
        ));

        let disabled = request(serde_json::json!("required"), Some(false));
        assert!(!OpenAIPreprocessor::guided_output_requires_reasoning(
            &disabled,
            Some("nemotron_v3")
        ));
        assert!(!OpenAIPreprocessor::guided_output_requires_reasoning(
            &required, None
        ));

        let automatic = request(serde_json::json!("auto"), Some(true));
        assert!(!OpenAIPreprocessor::guided_output_requires_reasoning(
            &automatic,
            Some("nemotron_v3")
        ));

        let gemma_without_opt_in = request(serde_json::json!("required"), None);
        assert!(!OpenAIPreprocessor::guided_output_requires_reasoning(
            &gemma_without_opt_in,
            Some("gemma4")
        ));
        let gemma_with_opt_in = request(serde_json::json!("required"), Some(true));
        assert!(OpenAIPreprocessor::guided_output_requires_reasoning(
            &gemma_with_opt_in,
            Some("gemma4")
        ));

        let deepseek_default = request(serde_json::json!("required"), None);
        assert!(OpenAIPreprocessor::guided_output_requires_reasoning(
            &deepseek_default,
            Some("deepseek_v4")
        ));
        let deepseek_disabled = request(serde_json::json!("required"), Some(false));
        assert!(!OpenAIPreprocessor::guided_output_requires_reasoning(
            &deepseek_disabled,
            Some("deepseek_v4")
        ));

        let structured_request = |enable_thinking: bool| {
            serde_json::from_value::<NvCreateChatCompletionRequest>(serde_json::json!({
                "model": "test-model",
                "messages": [{"role": "user", "content": "return json"}],
                "chat_template_kwargs": {"enable_thinking": enable_thinking},
                "response_format": {
                    "type": "json_schema",
                    "json_schema": {
                        "name": "result",
                        "schema": {"type": "object"}
                    }
                }
            }))
            .unwrap()
        };
        let json_object_request = |enable_thinking: bool| {
            serde_json::from_value::<NvCreateChatCompletionRequest>(serde_json::json!({
                "model": "test-model",
                "messages": [{"role": "user", "content": "return json"}],
                "chat_template_kwargs": {"enable_thinking": enable_thinking},
                "response_format": {"type": "json_object"}
            }))
            .unwrap()
        };
        assert!(OpenAIPreprocessor::guided_output_requires_reasoning(
            &structured_request(true),
            Some("qwen3")
        ));
        assert!(OpenAIPreprocessor::guided_output_requires_reasoning(
            &json_object_request(true),
            Some("qwen3")
        ));
        assert!(!OpenAIPreprocessor::guided_output_requires_reasoning(
            &structured_request(false),
            Some("qwen3")
        ));
        assert!(!OpenAIPreprocessor::guided_output_requires_reasoning(
            &structured_request(true),
            Some("gpt_oss")
        ));
        assert!(!OpenAIPreprocessor::guided_output_requires_reasoning(
            &json_object_request(true),
            Some("gpt_oss")
        ));
        assert!(!OpenAIPreprocessor::guided_output_requires_reasoning(
            &structured_request(false),
            Some("gpt_oss")
        ));
        assert!(OpenAIPreprocessor::guided_output_requires_reasoning(
            &request(serde_json::json!("required"), None),
            Some("gpt_oss")
        ));
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn exact_mm_routing_rejects_backend_owned_modalities() {
        assert!(exact_mm_routing_supports_modality("image_url", false));
        assert!(exact_mm_routing_supports_modality("video_url", true));
        assert!(!exact_mm_routing_supports_modality("video_url", false));
        assert!(!exact_mm_routing_supports_modality("audio_url", true));
    }

    /// Verifies SGLang's effective reasoning mode for each parser family.
    #[test]
    fn test_sglang_effective_reasoning_enabled() {
        let cases = [
            ("qwen3", serde_json::json!({}), true),
            (
                "qwen3",
                serde_json::json!({"enable_thinking": false}),
                false,
            ),
            ("nemotron_v3", serde_json::json!({}), true),
            ("gemma4", serde_json::json!({}), false),
            ("gemma4", serde_json::json!({"enable_thinking": true}), true),
            ("deepseek_v4", serde_json::json!({}), true),
            ("deepseek_v4", serde_json::json!({"thinking": true}), true),
            (
                "deepseek_v4",
                serde_json::json!({"enable_thinking": true}),
                true,
            ),
            (
                "deepseek_v4",
                serde_json::json!({"thinking_mode": "thinking"}),
                true,
            ),
            (
                "deepseek_v4",
                serde_json::json!({"enable_thinking": false}),
                false,
            ),
            (
                "deepseek_v4",
                serde_json::json!({"thinking_mode": "chat"}),
                false,
            ),
            (
                "deepseek_v4",
                serde_json::json!({"thinking": false, "thinking_mode": "thinking"}),
                false,
            ),
            ("deepseek_v3_2", serde_json::json!({}), true),
            ("deepseek_v3_1", serde_json::json!({}), false),
            (
                "deepseek_v3_1",
                serde_json::json!({"enable_thinking": true}),
                true,
            ),
            ("kimi_k25", serde_json::json!({"thinking": false}), false),
            ("kimi_k3", serde_json::json!({}), true),
            ("kimi-k3", serde_json::json!({"thinking": false}), false),
            ("minimax_m2", serde_json::json!({}), true),
            ("minimax_m2", serde_json::json!({"thinking": false}), false),
            ("mistral", serde_json::json!({}), false),
            (
                "mistral",
                serde_json::json!({"reasoning_effort": "none"}),
                false,
            ),
            (
                "mistral",
                serde_json::json!({"reasoning_effort": "high"}),
                true,
            ),
            ("minimax_m3", serde_json::json!({}), true),
            (
                "minimax_m3",
                serde_json::json!({"thinking_mode": "disabled"}),
                false,
            ),
            ("gpt_oss", serde_json::json!({}), true),
            (
                "gpt_oss",
                serde_json::json!({"enable_thinking": true}),
                true,
            ),
            (
                "gpt_oss",
                serde_json::json!({"enable_thinking": false}),
                true,
            ),
            ("deepseek_r1", serde_json::json!({}), true),
            ("minimax_append_think", serde_json::json!({}), false),
            ("basic", serde_json::json!({}), false),
        ];

        for (parser, request_args, expected) in cases {
            let request_args = serde_json::from_value(request_args).unwrap();
            assert_eq!(
                OpenAIPreprocessor::sglang_effective_reasoning_enabled(
                    Some(parser),
                    Some(&request_args),
                ),
                expected,
                "parser={parser}, args={request_args:?}",
            );
        }
        assert!(!OpenAIPreprocessor::sglang_effective_reasoning_enabled(
            None, None
        ));
    }

    #[test]
    fn test_internal_preserve_omitted_max_tokens_option() {
        assert_eq!(
            OpenAIPreprocessor::omitted_max_tokens_default(
                10,
                Some(100),
                PreprocessRequestOptions::default()
            ),
            Some(90)
        );
        assert_eq!(
            OpenAIPreprocessor::omitted_max_tokens_default(
                10,
                Some(100),
                PreprocessRequestOptions {
                    preserve_omitted_max_tokens: true,
                },
            ),
            None
        );
        assert_eq!(
            OpenAIPreprocessor::omitted_max_tokens_default(
                10,
                None,
                PreprocessRequestOptions::default()
            ),
            None
        );
        assert_eq!(
            OpenAIPreprocessor::omitted_max_tokens_default(
                10,
                Some(0),
                PreprocessRequestOptions::default()
            ),
            Some(0)
        );
    }

    #[test]
    fn test_exact_prompt_len() {
        let images = MultimodalDataMap::from([(
            "image_url".to_string(),
            vec![MultimodalData::Url(
                url::Url::parse("https://example.com/image.png").unwrap(),
            )],
        )]);
        let videos = MultimodalDataMap::from([(
            "video_url".to_string(),
            vec![MultimodalData::Url(
                url::Url::parse("https://example.com/video.mp4").unwrap(),
            )],
        )]);
        let mixed = MultimodalDataMap::from([
            (
                "image_url".to_string(),
                vec![MultimodalData::Url(
                    url::Url::parse("https://example.com/image.png").unwrap(),
                )],
            ),
            (
                "audio_url".to_string(),
                vec![MultimodalData::Url(
                    url::Url::parse("https://example.com/audio.wav").unwrap(),
                )],
            ),
        ]);

        // Image-expanded length present: use it instead of placeholder tokens.
        assert_eq!(
            OpenAIPreprocessor::exact_prompt_len(Some(500), Some(&images), 12),
            Some(500)
        );
        // Expanded length 0 (serde-default / absent) with images: defer to backend.
        assert_eq!(
            OpenAIPreprocessor::exact_prompt_len(Some(0), Some(&images), 12),
            None
        );
        // No routing info but images present: defer to backend.
        assert_eq!(
            OpenAIPreprocessor::exact_prompt_len(None, Some(&images), 12),
            None
        );
        // Video/audio expansion is backend-owned, including mixed requests
        // whose routing metadata expands only the image portion.
        assert_eq!(
            OpenAIPreprocessor::exact_prompt_len(None, Some(&videos), 12),
            None
        );
        assert_eq!(
            OpenAIPreprocessor::exact_prompt_len(Some(500), Some(&mixed), 12),
            None
        );
        // Text-only: use the token count.
        assert_eq!(
            OpenAIPreprocessor::exact_prompt_len(None, None, 12),
            Some(12)
        );
        // Expanded length 0 without media: fall back to the token count.
        assert_eq!(
            OpenAIPreprocessor::exact_prompt_len(Some(0), None, 12),
            Some(12)
        );
    }

    fn token_budget(
        combined_limit: u32,
        reject_prompt_overflow: bool,
        reject_total_overflow: bool,
    ) -> TokenBudget {
        TokenBudget {
            combined_limit,
            reject_prompt_overflow,
            reject_total_overflow,
        }
    }

    #[test]
    fn test_requested_token_budget_policy_matrix() {
        let cases = [
            // Exact combined limit is accepted.
            (3, Some(7), true, true, false),
            // One token beyond the combined limit is rejected.
            (3, Some(8), true, true, true),
            // A full prompt leaves no room for generation.
            (10, None, true, true, true),
            // Each rejection dimension can be delegated independently.
            (3, Some(8), true, false, false),
            (10, Some(8), false, true, false),
        ];

        for (
            prompt_len,
            max_tokens,
            reject_prompt_overflow,
            reject_total_overflow,
            should_reject,
        ) in cases
        {
            let budget = token_budget(10, reject_prompt_overflow, reject_total_overflow);
            assert_eq!(
                OpenAIPreprocessor::validate_requested_token_budget(
                    prompt_len,
                    max_tokens,
                    Some(&budget),
                )
                .is_err(),
                should_reject,
            );
        }
    }

    fn preprocessed_budget_request(max_tokens: Option<u32>) -> PreprocessedRequest {
        let stop_conditions = crate::protocols::common::StopConditions {
            max_tokens,
            ..Default::default()
        };

        PreprocessedRequest::builder()
            .model("test-model".to_string())
            .token_ids(vec![1, 2, 3])
            .stop_conditions(stop_conditions)
            .sampling_options(crate::protocols::common::SamplingOptions::default())
            .output_options(crate::protocols::common::OutputOptions::default())
            .build()
            .unwrap()
    }

    #[test]
    fn attach_agent_context_forwards_opaque_headers() {
        let agent_context = AgentContext {
            session_id: "codex-thread".to_string(),
            parent_session_id: None,
            session_final: None,
            agent_headers: std::collections::BTreeMap::from([(
                "x-claude-code-future".into(),
                vec!["unknown".into(), "second".into()],
            )])
            .into(),
            input_trigger: None,
        };
        let mut context = PipelineContext::new(());
        context.insert(AGENT_CONTEXT_CONTEXT_KEY, agent_context.clone());
        let mut request = preprocessed_budget_request(None);

        attach_agent_context_from_context(&mut request, &context);

        assert_eq!(request.agent_context.as_ref(), Some(&agent_context));
        let wire = serde_json::to_value(&request).unwrap();
        assert_eq!(
            wire["agent_context"]["agent_headers"]["x-claude-code-future"],
            serde_json::json!(["unknown", "second"])
        );
        let restored: AgentContext = serde_json::from_value(wire["agent_context"].clone()).unwrap();
        assert_eq!(restored, agent_context);
        assert!(std::sync::Arc::ptr_eq(
            &request.agent_context.as_ref().unwrap().agent_headers,
            &agent_context.agent_headers
        ));
    }

    #[test]
    fn attach_request_context_metadata_keeps_affinity_separate_from_agent_context() {
        use crate::protocols::common::extensions::{
            SESSION_AFFINITY_CONTEXT_KEY, SessionAffinityId,
        };

        let agent_context = AgentContext {
            session_id: "agent-session".to_string(),
            parent_session_id: Some("agent-parent".to_string()),
            session_final: None,
            agent_headers: Default::default(),
            input_trigger: None,
        };
        let mut context = PipelineContext::new(());
        context.insert(AGENT_CONTEXT_CONTEXT_KEY, agent_context.clone());
        context.insert(
            SESSION_AFFINITY_CONTEXT_KEY,
            SessionAffinityId::new("routing-session"),
        );
        let mut request = preprocessed_budget_request(None);

        attach_request_context_metadata(&mut request, &context);

        assert_eq!(request.agent_context.as_ref(), Some(&agent_context));
        assert_eq!(
            request.image_cache_scope.as_deref(),
            Some("routing-session")
        );
        let wire = serde_json::to_value(&request).unwrap();
        assert_eq!(wire["agent_context"]["session_id"], "agent-session");
        assert_eq!(wire["image_cache_scope"], "routing-session");

        let mut affinity_only_context = PipelineContext::new(());
        affinity_only_context.insert(
            SESSION_AFFINITY_CONTEXT_KEY,
            SessionAffinityId::new("routing-only"),
        );
        let mut affinity_only_request = preprocessed_budget_request(None);
        attach_request_context_metadata(&mut affinity_only_request, &affinity_only_context);
        assert!(affinity_only_request.agent_context.is_none());
        assert_eq!(
            affinity_only_request.image_cache_scope.as_deref(),
            Some("routing-only")
        );
    }

    #[test]
    fn test_preprocessed_completion_budget_validation_and_deferral() {
        let text_request = preprocessed_budget_request(Some(8));
        let reject = token_budget(10, true, true);
        assert!(
            OpenAIPreprocessor::validate_preprocessed_token_budget(&text_request, Some(&reject))
                .is_err()
        );

        let defer_total = token_budget(10, true, false);
        assert_eq!(
            OpenAIPreprocessor::validate_preprocessed_token_budget(
                &text_request,
                Some(&defer_total),
            )
            .unwrap(),
            Some(3)
        );

        // Prompt embeddings do not expose their sequence length as token_ids.
        let mut embeddings_request = text_request.clone();
        embeddings_request.prompt_embeds = Some("opaque-tensor".to_string());
        assert_eq!(
            OpenAIPreprocessor::validate_preprocessed_token_budget(
                &embeddings_request,
                Some(&reject),
            )
            .unwrap(),
            None
        );
    }

    struct UnreachableBackend;

    #[async_trait]
    impl AsyncEngine<SingleIn<PreprocessedRequest>, ManyOut<Annotated<BackendOutput>>, Error>
        for UnreachableBackend
    {
        async fn generate(
            &self,
            _request: SingleIn<PreprocessedRequest>,
        ) -> Result<ManyOut<Annotated<BackendOutput>>, Error> {
            panic!("request must be rejected before backend dispatch")
        }
    }

    #[tokio::test]
    async fn test_completion_operator_rejects_token_budget_overflow() {
        let mut mdc = ModelDeploymentCard::load_from_disk(
            "tests/data/sample-models/mock-llama-3.1-8b-instruct",
            None,
        )
        .unwrap();
        mdc.runtime_config.context_length = Some(100);
        mdc.runtime_config
            .set_engine_specific(TOKEN_BUDGET_RUNTIME_KEY, token_budget(10, true, true))
            .unwrap();
        let preprocessor = OpenAIPreprocessor::new(mdc).unwrap();

        let request = NvCreateCompletionRequest {
            inner: dynamo_protocols::types::CreateCompletionRequest {
                model: "test-model".to_string(),
                prompt: dynamo_protocols::types::Prompt::IntegerArray(vec![1, 2, 3]),
                max_tokens: Some(8),
                ..Default::default()
            },
            common: Default::default(),
            nvext: None,
            metadata: None,
            return_tokens_as_token_ids: None,
            no_stop_trim: None,
            unsupported_fields: Default::default(),
        };
        let next: Arc<
            dyn AsyncEngine<SingleIn<PreprocessedRequest>, ManyOut<Annotated<BackendOutput>>, Error>,
        > = Arc::new(UnreachableBackend);

        let result =
            Operator::generate(preprocessor.as_ref(), PipelineContext::new(request), next).await;
        let Err(err) = result else {
            panic!("over-budget completion should fail admission");
        };
        let dynamo_err = err
            .downcast_ref::<DynamoError>()
            .expect("error should preserve the DynamoError type");
        assert_eq!(dynamo_err.error_type(), ErrorType::InvalidArgument);
    }

    #[tokio::test]
    async fn test_completion_operator_rejects_out_of_range_token_prompt() {
        let mdc = ModelDeploymentCard::load_from_disk(
            "tests/data/sample-models/mock-llama-3.1-8b-instruct",
            None,
        )
        .unwrap();
        let preprocessor = OpenAIPreprocessor::new(mdc).unwrap();
        // The card's vocab_size is 128256.
        let request: NvCreateCompletionRequest = serde_json::from_value(serde_json::json!({
            "model": "test-model",
            "prompt": [1, 128256]
        }))
        .unwrap();
        let next: Arc<
            dyn AsyncEngine<SingleIn<PreprocessedRequest>, ManyOut<Annotated<BackendOutput>>, Error>,
        > = Arc::new(UnreachableBackend);

        let result =
            Operator::generate(preprocessor.as_ref(), PipelineContext::new(request), next).await;
        let Err(err) = result else {
            panic!("an out-of-range token prompt should fail before dispatch");
        };
        let dynamo_err = err
            .downcast_ref::<DynamoError>()
            .expect("error should preserve the DynamoError type");
        assert_eq!(dynamo_err.error_type(), ErrorType::InvalidArgument);
    }

    #[tokio::test]
    async fn chat_operator_rejects_invalid_legacy_jail_request_before_dispatch() {
        let mut mdc = ModelDeploymentCard::load_from_disk(
            "tests/data/sample-models/mock-llama-3.1-8b-instruct",
            None,
        )
        .unwrap();
        mdc.runtime_config.tool_call_parser = Some("hermes".to_string());
        let preprocessor = OpenAIPreprocessor::new(mdc).unwrap();
        let request: NvCreateChatCompletionRequest = serde_json::from_value(serde_json::json!({
            "model": "test-model",
            "messages": [{"role": "user", "content": "What is the weather?"}],
            "n": 2,
            "max_tokens": 4,
            "tools": [{
                "type": "function",
                "function": {
                    "name": "get_weather",
                    "description": "Get the weather",
                    "parameters": {"type": "object", "properties": {}}
                }
            }],
            "tool_choice": "auto",
            "nvext": {"extra_fields": ["engine_data"]}
        }))
        .unwrap();
        let next: Arc<
            dyn AsyncEngine<SingleIn<PreprocessedRequest>, ManyOut<Annotated<BackendOutput>>, Error>,
        > = Arc::new(UnreachableBackend);

        let result =
            Operator::generate(preprocessor.as_ref(), PipelineContext::new(request), next).await;
        let Err(err) = result else {
            panic!("invalid legacy-jail request should fail admission");
        };
        let dynamo_err = err
            .downcast_ref::<DynamoError>()
            .expect("error should preserve the DynamoError type");
        assert_eq!(dynamo_err.error_type(), ErrorType::InvalidArgument);
        assert!(dynamo_err.to_string().contains("legacy tool-call parsing"));
    }

    fn test_prompt_formatter(template: &str) -> Arc<dyn OAIPromptFormatter> {
        let template: dynamo_renderer::ChatTemplate = serde_json::from_value(serde_json::json!({
            "chat_template": template
        }))
        .unwrap();
        match dynamo_renderer::PromptFormatter::from_parts(
            template,
            dynamo_renderer::ContextMixins::default(),
            false,
        )
        .unwrap()
        {
            dynamo_renderer::PromptFormatter::OAI(formatter) => formatter,
        }
    }

    fn assistant_only_request() -> NvCreateChatCompletionRequest {
        serde_json::from_value(serde_json::json!({
            "model": "test-model",
            "messages": [{"role": "assistant", "content": "prefill"}]
        }))
        .unwrap()
    }

    const REQUIRES_USER_TEMPLATE: &str = "\
        {% set ns = namespace(has_user=false) %}\
        {% for message in messages %}\
            {% if message['role'] == 'user' %}{% set ns.has_user = true %}{% endif %}\
        {% endfor %}\
        {% if not ns.has_user %}{{ raise_exception('No user query found in messages.') }}{% endif %}\
        {{ messages[0]['content'] }}";

    const REQUIRES_LEADING_SYSTEM_TEMPLATE: &str = "\
        {%- for message in messages -%}\
            {%- if message['role'] == 'system' and not loop.first -%}\
                {{- raise_exception('System message must be at the beginning.') -}}\
            {%- endif -%}\
            {{- message['role'] }}:{{ message['content'] }}\n\
        {%- endfor -%}";

    fn render_through_preprocessor(
        formatter: &dyn OAIPromptFormatter,
        request: &dyn OAIChatLikeRequest,
    ) -> Result<RenderedPrompt> {
        formatter
            .render_prompt(request)
            .map_err(OpenAIPreprocessor::map_prompt_render_error)
    }

    #[test]
    fn test_assistant_only_request_accepted_when_template_accepts_it() {
        let formatter = test_prompt_formatter(
            "{% for message in messages %}{{ message['role'] }}:{{ message['content'] }}{% endfor %}",
        );

        let rendered =
            render_through_preprocessor(formatter.as_ref(), &assistant_only_request()).unwrap();

        assert_eq!(rendered.as_str(), "assistant:prefill");
    }

    #[test]
    fn test_assistant_only_template_error_is_invalid_argument() {
        let formatter = test_prompt_formatter(REQUIRES_USER_TEMPLATE);

        let error = render_through_preprocessor(formatter.as_ref(), &assistant_only_request())
            .context("Failed to apply prompt template")
            .unwrap_err();
        let dynamo_error = error
            .chain()
            .find_map(|cause| cause.downcast_ref::<DynamoError>())
            .expect("template render error should be classified as a DynamoError");

        assert_eq!(dynamo_error.error_type(), ErrorType::InvalidArgument);
        assert!(
            dynamo_error
                .message()
                .contains("No user query found in messages.")
        );
    }

    #[test]
    fn test_restrictive_template_accepts_request_with_user_message() {
        let formatter = test_prompt_formatter(REQUIRES_USER_TEMPLATE);
        let request: NvCreateChatCompletionRequest = serde_json::from_value(serde_json::json!({
            "model": "test-model",
            "messages": [{"role": "user", "content": "hello"}]
        }))
        .unwrap();

        let rendered = render_through_preprocessor(formatter.as_ref(), &request).unwrap();

        assert_eq!(rendered.as_str(), "hello");
    }

    #[test]
    fn test_nonleading_system_message_normalized_for_strict_template() {
        let formatter = test_prompt_formatter(REQUIRES_LEADING_SYSTEM_TEMPLATE);
        let anthropic_request: crate::protocols::anthropic::AnthropicCreateMessageRequest =
            serde_json::from_value(serde_json::json!({
                "model": "test-model",
                "max_tokens": 100,
                "system": "You are Claude Code.",
                "messages": [
                    {"role": "user", "content": "Run make test."},
                    {"role": "system", "content": "Available agent types and skills."}
                ],
                "tools": [{
                    "name": "Bash",
                    "description": "Run a shell command",
                    "input_schema": {
                        "type": "object",
                        "properties": {
                            "command": {"type": "string"}
                        },
                        "required": ["command"]
                    }
                }]
            }))
            .unwrap();
        let request: NvCreateChatCompletionRequest = anthropic_request.try_into().unwrap();

        let rendered = render_through_preprocessor(formatter.as_ref(), &request).unwrap();

        assert_eq!(
            rendered.as_str(),
            "system:You are Claude Code.\
             user:Run make test.\
             user:Available agent types and skills."
        );
    }

    #[test]
    fn continue_final_message_leaves_last_assistant_open_on_llama_template() {
        let mut mdc = ModelDeploymentCard::load_from_disk(
            "tests/data/sample-models/mock-llama-3.1-8b-instruct",
            None,
        )
        .unwrap();
        mdc.set_name("test-model");
        let preprocessor = OpenAIPreprocessor::new(mdc).unwrap();

        let default_request: NvCreateChatCompletionRequest =
            serde_json::from_value(serde_json::json!({
                "model": "test-model",
                "messages": [
                    {"role": "user", "content": "Continue this sentence"},
                    {"role": "assistant", "content": "LLM-Native Interaction"}
                ]
            }))
            .unwrap();
        let continue_request: NvCreateChatCompletionRequest =
            serde_json::from_value(serde_json::json!({
                "model": "test-model",
                "messages": [
                    {"role": "user", "content": "Continue this sentence"},
                    {"role": "assistant", "content": "LLM-Native Interaction"}
                ],
                "add_generation_prompt": false,
                "continue_final_message": true
            }))
            .unwrap();

        let default_prompt = preprocessor
            .apply_template(&default_request)
            .unwrap()
            .unwrap();
        let continue_prompt = preprocessor
            .apply_template(&continue_request)
            .unwrap()
            .unwrap();

        assert!(
            default_prompt
                .as_str()
                .ends_with("<|start_header_id|>assistant<|end_header_id|>\n\n"),
            "default render should start a new assistant turn, got {:?}",
            default_prompt.as_str()
        );
        assert!(
            continue_prompt.as_str().ends_with("LLM-Native Interaction"),
            "continue_final_message should leave the last assistant open, got {:?}",
            continue_prompt.as_str()
        );
        assert!(
            !continue_prompt.as_str().contains(
                "LLM-Native Interaction<|eot_id|><|start_header_id|>assistant<|end_header_id|>"
            ),
            "continue_final_message must not close the last assistant and start a new turn, got {:?}",
            continue_prompt.as_str()
        );
    }

    #[test]
    fn should_add_generation_prompt_defaults_true_and_continue_forces_false() {
        use dynamo_renderer::OAIChatLikeRequest;

        let unset: NvCreateChatCompletionRequest = serde_json::from_value(serde_json::json!({
            "model": "test-model",
            "messages": [{"role": "user", "content": "hi"}]
        }))
        .unwrap();
        assert!(unset.should_add_generation_prompt());

        let explicit_false: NvCreateChatCompletionRequest =
            serde_json::from_value(serde_json::json!({
                "model": "test-model",
                "messages": [{"role": "user", "content": "hi"}],
                "add_generation_prompt": false
            }))
            .unwrap();
        assert!(!explicit_false.should_add_generation_prompt());

        let continue_with_false: NvCreateChatCompletionRequest =
            serde_json::from_value(serde_json::json!({
                "model": "test-model",
                "messages": [
                    {"role": "user", "content": "hi"},
                    {"role": "assistant", "content": "partial"}
                ],
                "add_generation_prompt": false,
                "continue_final_message": true
            }))
            .unwrap();
        assert!(!continue_with_false.should_add_generation_prompt());
    }

    /// Qwen-style templates close every turn, including the last assistant. Truncate
    /// after render is what actually leaves the prefix open; Llama's mock template
    /// already omits the last eot when `add_generation_prompt` is false.
    const QWEN_STYLE_TEMPLATE: &str = "\
{%- for message in messages -%}\
{%- if message.role == 'user' -%}{{ '<|im_start|>user\n' + message.content + '<|im_end|>\n' }}\
{%- elif message.role == 'assistant' -%}{{ '<|im_start|>assistant\n' + message.content + '<|im_end|>\n' }}\
{%- endif -%}\
{%- endfor -%}\
{%- if add_generation_prompt -%}{{ '<|im_start|>assistant\n' }}{%- endif -%}";

    fn continue_request() -> NvCreateChatCompletionRequest {
        serde_json::from_value(serde_json::json!({
            "model": "test-model",
            "messages": [
                {"role": "user", "content": "Continue this sentence"},
                {"role": "assistant", "content": "LLM-Native Interaction"}
            ],
            "add_generation_prompt": false,
            "continue_final_message": true
        }))
        .unwrap()
    }

    #[test]
    fn normalized_args_request_preserves_render_transformations() {
        use crate::preprocessor::prompt::CONTINUE_FINAL_MESSAGE_TAG;

        let request: NvCreateChatCompletionRequest = serde_json::from_value(serde_json::json!({
            "model": "test-model",
            "messages": [{"role": "assistant", "content": "prefix", "tool_calls": [{
                "id": "c1", "type": "function",
                "function": {"name": "lookup", "arguments": "{\"query\":\"hello\"}"}
            }]}]
        }))
        .unwrap();
        let normalized = NormalizedArgsRequest {
            inner: &request,
            normalize_tool_call_args: true,
            continue_final_message: true,
        };
        assert!(request.typed_messages().is_some());
        assert!(normalized.typed_messages().is_none());

        let messages = serde_json::to_value(normalized.messages()).unwrap();
        assert_eq!(
            messages[0]["tool_calls"][0]["function"]["arguments"],
            serde_json::json!({"query": "hello"})
        );
        assert_eq!(
            messages[0]["content"],
            format!("prefix{CONTINUE_FINAL_MESSAGE_TAG}")
        );
        // Keep string arguments in the template so only the adapter can perform
        // this conversion; the renderer's own argument normalization is bypassed.
        let formatter = test_prompt_formatter(
            "{% for m in messages %}{% for call in m.tool_calls %}{% if call.function.arguments is string %}RAW{% else %}{{ call.function.arguments.query }}{% endif %}{% endfor %}{{ m.content }}{% endfor %}",
        );
        let rendered = formatter.render_prompt(&normalized).unwrap();
        assert_eq!(
            rendered.as_str(),
            format!("helloprefix{CONTINUE_FINAL_MESSAGE_TAG}")
        );
        assert_eq!(
            apply_continue_final_message(rendered).unwrap().as_str(),
            "helloprefix"
        );
    }

    fn render_with_continue_final_message(
        formatter: &dyn OAIPromptFormatter,
        request: &NvCreateChatCompletionRequest,
    ) -> RenderedPrompt {
        use crate::protocols::openai::common_ext::CommonExtProvider;

        let continue_final = request.get_continue_final_message() == Some(true);
        let rendered = if continue_final {
            formatter
                .render_prompt(&NormalizedArgsRequest {
                    inner: request,
                    normalize_tool_call_args: false,
                    continue_final_message: true,
                })
                .unwrap()
        } else {
            formatter.render_prompt(request).unwrap()
        };
        if continue_final {
            apply_continue_final_message(rendered).unwrap()
        } else {
            rendered
        }
    }

    #[test]
    fn continue_final_message_strips_qwen_style_closing_tokens() {
        let formatter = test_prompt_formatter(QWEN_STYLE_TEMPLATE);
        let default_request: NvCreateChatCompletionRequest =
            serde_json::from_value(serde_json::json!({
                "model": "test-model",
                "messages": [
                    {"role": "user", "content": "Continue this sentence"},
                    {"role": "assistant", "content": "LLM-Native Interaction"}
                ]
            }))
            .unwrap();
        let closed_only: NvCreateChatCompletionRequest =
            serde_json::from_value(serde_json::json!({
                "model": "test-model",
                "messages": [
                    {"role": "user", "content": "Continue this sentence"},
                    {"role": "assistant", "content": "LLM-Native Interaction"}
                ],
                "add_generation_prompt": false
            }))
            .unwrap();

        let default_prompt =
            render_with_continue_final_message(formatter.as_ref(), &default_request);
        let closed_prompt = render_with_continue_final_message(formatter.as_ref(), &closed_only);
        let continue_prompt =
            render_with_continue_final_message(formatter.as_ref(), &continue_request());

        assert!(
            default_prompt.as_str().ends_with("<|im_start|>assistant\n"),
            "default Qwen render should start a new assistant turn, got {:?}",
            default_prompt.as_str()
        );
        assert!(
            closed_prompt
                .as_str()
                .ends_with("LLM-Native Interaction<|im_end|>\n"),
            "add_generation_prompt=false alone must still close the last assistant, got {:?}",
            closed_prompt.as_str()
        );
        assert_eq!(
            continue_prompt.as_str(),
            "<|im_start|>user\nContinue this sentence<|im_end|>\n<|im_start|>assistant\nLLM-Native Interaction"
        );
    }

    /// Raw turns `same / previous / same`; the template uppercases the last
    /// turn to `SAME`. Searching rendered text for `same` would cut at the
    /// first copy. The HuggingFace marker must keep the full conversation.
    #[test]
    fn continue_final_message_marker_survives_rewritten_final_turn() {
        const TEMPLATE: &str = "\
{%- for message in messages -%}\
{%- if loop.last -%}{{ message.content | upper }}|{%- else -%}{{ message.content }}|{%- endif -%}\
{%- endfor -%}closed";
        let formatter = test_prompt_formatter(TEMPLATE);
        let request: NvCreateChatCompletionRequest = serde_json::from_value(serde_json::json!({
            "model": "test-model",
            "messages": [
                {"role": "user", "content": "same"},
                {"role": "user", "content": "previous"},
                {"role": "assistant", "content": "same"}
            ],
            "add_generation_prompt": false,
            "continue_final_message": true
        }))
        .unwrap();

        let prompt = render_with_continue_final_message(formatter.as_ref(), &request);
        assert_eq!(prompt.as_str(), "same|previous|SAME");
        assert!(
            !prompt.as_str().contains("CONTINUE_FINAL_MESSAGE_TAG"),
            "marker must be stripped from the prompt sent to the model, got {:?}",
            prompt.as_str()
        );
    }

    #[test]
    fn test_kimi_thinking_normalization_keeps_template_and_gates_in_sync() {
        let template: dynamo_renderer::ChatTemplate = serde_json::from_value(serde_json::json!({
            "chat_template": "{% if thinking == true and enable_thinking == true %}<think>{% elif thinking == false and enable_thinking == false %}<chat>{% else %}<mismatch>{% endif %}"
        }))
        .unwrap();
        let formatter = dynamo_renderer::PromptFormatter::from_parts(
            template,
            dynamo_renderer::ContextMixins::default(),
            false,
        )
        .unwrap();
        let cases = [
            (serde_json::json!(true), true, "true"),
            (serde_json::json!("true"), true, "string true"),
            (serde_json::json!("TRUE"), true, "uppercase true"),
            (serde_json::json!("1"), true, "string one"),
            (serde_json::json!("yes"), true, "yes"),
            (serde_json::json!("on"), true, "on"),
            (serde_json::json!(1), true, "one"),
            (serde_json::json!(false), false, "false"),
            (serde_json::json!("false"), false, "string false"),
            (serde_json::json!("no"), false, "no"),
            (serde_json::json!("off"), false, "off"),
            (serde_json::json!(0), false, "zero"),
        ];

        let assert_case =
            |key: Option<&str>, value: Option<&serde_json::Value>, expected, description| {
                let mut request: NvCreateChatCompletionRequest =
                    serde_json::from_value(serde_json::json!({
                        "messages": [{"role": "user", "content": "hello"}],
                        "model": "moonshotai/Kimi-K2.5-Instruct"
                    }))
                    .unwrap();
                if let (Some(key), Some(value)) = (key, value) {
                    request.chat_template_args = Some(std::collections::HashMap::from([(
                        key.to_string(),
                        value.clone(),
                    )]));
                }

                OpenAIPreprocessor::normalize_thinking_arg(&mut request, Some("kimi_k25"), None);
                let args = request.chat_template_args.as_ref();
                assert_eq!(
                    dynamo_renderer::thinking_bool_from_args(args),
                    Some(expected),
                    "renderer helper mismatch for {description}"
                );
                assert_eq!(
                    OpenAIPreprocessor::sglang_effective_reasoning_enabled(Some("kimi_k25"), args),
                    expected,
                    "SGLang gate mismatch for {description}"
                );
                assert_eq!(
                    !OpenAIPreprocessor::is_reasoning_disabled_by_request(Some("kimi_k25"), args,),
                    expected,
                    "postprocessor gate mismatch for {description}"
                );

                let rendered = match &formatter {
                    dynamo_renderer::PromptFormatter::OAI(formatter) => {
                        formatter.render(&request).unwrap()
                    }
                };
                assert_eq!(
                    rendered,
                    if expected { "<think>" } else { "<chat>" },
                    "rendered prompt mismatch for {description}"
                );
            };

        for key in ["thinking", "enable_thinking"] {
            for (value, expected, description) in &cases {
                assert_case(Some(key), Some(value), *expected, *description);
            }
        }
        assert_case(None, None, true, "omitted Kimi default");

        for parser in ["kimi_k3", "kimi-k3"] {
            let mut request: NvCreateChatCompletionRequest =
                serde_json::from_value(serde_json::json!({
                    "messages": [{"role": "user", "content": "hello"}],
                    "model": "moonshotai/Kimi-K3"
                }))
                .unwrap();
            OpenAIPreprocessor::normalize_thinking_arg(&mut request, Some(parser), None);
            assert_eq!(
                dynamo_renderer::thinking_bool_from_args(request.chat_template_args.as_ref()),
                Some(true),
                "{parser} should default to thinking mode",
            );
        }

        let mut conflicting_request: NvCreateChatCompletionRequest =
            serde_json::from_value(serde_json::json!({
                "messages": [{"role": "user", "content": "hello"}],
                "model": "moonshotai/Kimi-K2.5-Instruct",
                "chat_template_kwargs": {
                    "thinking": "true",
                    "enable_thinking": false
                }
            }))
            .unwrap();
        OpenAIPreprocessor::normalize_thinking_arg(
            &mut conflicting_request,
            Some("kimi_k25"),
            None,
        );
        let args = conflicting_request.chat_template_args.as_ref().unwrap();
        assert_eq!(args.get("thinking"), Some(&serde_json::Value::Bool(true)));
        assert_eq!(
            args.get("enable_thinking"),
            Some(&serde_json::Value::Bool(true))
        );
        let rendered = match &formatter {
            dynamo_renderer::PromptFormatter::OAI(formatter) => {
                formatter.render(&conflicting_request).unwrap()
            }
        };
        assert_eq!(rendered, "<think>");
    }

    #[test]
    fn test_named_kimi_k3_normalizes_every_reasoning_consumer_to_disabled() {
        for parser in ["kimi_k3", "kimi-k3"] {
            let mut request: NvCreateChatCompletionRequest =
                serde_json::from_value(serde_json::json!({
                    "messages": [{"role": "user", "content": "Weather in Berlin?"}],
                    "model": "moonshotai/Kimi-K3",
                    "tools": [{
                        "type": "function",
                        "function": {
                            "name": "get_weather",
                            "parameters": {
                                "type": "object",
                                "properties": {"city": {"type": "string"}}
                            }
                        }
                    }],
                    "tool_choice": {
                        "type": "function",
                        "function": {"name": "get_weather"}
                    },
                    "reasoning_effort": "high",
                    "chat_template_args": {
                        "thinking": true,
                        "enable_thinking": true
                    }
                }))
                .unwrap();

            request.normalize_reasoning_template_args().unwrap();
            OpenAIPreprocessor::normalize_thinking_arg(&mut request, Some(parser), None);
            OpenAIPreprocessor::normalize_kimi_k3_named_tool_choice(&mut request, Some(parser));

            let args = request.chat_template_args.as_ref().unwrap();
            assert_eq!(args.get("thinking"), Some(&serde_json::Value::Bool(false)));
            assert_eq!(
                args.get("enable_thinking"),
                Some(&serde_json::Value::Bool(false))
            );
            assert_eq!(
                args.get("reasoning_effort"),
                Some(&serde_json::Value::String("high".to_string())),
                "the public effort value is preserved while the named-call exception disables thinking"
            );
            assert!(OpenAIPreprocessor::is_reasoning_disabled_by_request(
                Some(parser),
                Some(args)
            ));
        }
    }

    #[test]
    fn test_named_kimi_k3_override_is_scoped_to_k3_tool_parser() {
        let request_json = serde_json::json!({
            "messages": [{"role": "user", "content": "Weather?"}],
            "model": "test",
            "tools": [{
                "type": "function",
                "function": {
                    "name": "get_weather",
                    "parameters": {"type": "object"}
                }
            }],
            "tool_choice": {
                "type": "function",
                "function": {"name": "get_weather"}
            },
            "chat_template_args": {"thinking": true}
        });

        for parser in [None, Some("hermes"), Some("kimi_k2")] {
            let mut request: NvCreateChatCompletionRequest =
                serde_json::from_value(request_json.clone()).unwrap();
            OpenAIPreprocessor::normalize_kimi_k3_named_tool_choice(&mut request, parser);
            assert_eq!(
                dynamo_renderer::thinking_bool_from_args(request.chat_template_args.as_ref()),
                Some(true),
                "parser {parser:?} must retain its existing policy"
            );
        }
    }

    fn minimax_m3_request(body: serde_json::Value) -> NvCreateChatCompletionRequest {
        let mut base = serde_json::json!({
            "model": "MiniMaxAI/MiniMax-M3",
            "messages": [{"role": "user", "content": "hi"}],
        });
        let base_obj = base.as_object_mut().unwrap();
        for (k, v) in body.as_object().unwrap() {
            base_obj.insert(k.clone(), v.clone());
        }
        serde_json::from_value(base).unwrap()
    }

    #[test]
    fn test_normalize_thinking_arg_m3_defaults_disabled_for_json_schema() {
        for parser in ["minimax_m3", "minimax-m3"] {
            let mut request = minimax_m3_request(serde_json::json!({
                "response_format": {
                    "type": "json_schema",
                    "json_schema": {"name": "s", "schema": {"type": "object"}}
                }
            }));
            OpenAIPreprocessor::normalize_thinking_arg(&mut request, Some(parser), None);
            let args = request.chat_template_args.as_ref().unwrap();
            assert_eq!(
                args.get("thinking_mode"),
                Some(&serde_json::json!("disabled"))
            );
            assert_eq!(args.get("thinking"), Some(&serde_json::json!(false)));
            assert_eq!(args.get("enable_thinking"), Some(&serde_json::json!(false)));
        }
    }

    #[test]
    fn test_normalize_thinking_arg_m3_defaults_disabled_for_tool_choice_required() {
        let mut request = minimax_m3_request(serde_json::json!({
            "tools": [{"type": "function", "function": {"name": "f", "parameters": {"type": "object"}}}],
            "tool_choice": "required"
        }));
        OpenAIPreprocessor::normalize_thinking_arg(&mut request, Some("minimax_m3"), None);
        let args = request.chat_template_args.as_ref().unwrap();
        assert_eq!(
            args.get("thinking_mode"),
            Some(&serde_json::json!("disabled"))
        );
    }

    #[test]
    fn test_normalize_thinking_arg_m3_defaults_disabled_for_named_tool_choice() {
        let mut request = minimax_m3_request(serde_json::json!({
            "tools": [{"type": "function", "function": {"name": "f", "parameters": {"type": "object"}}}],
            "tool_choice": {"type": "function", "function": {"name": "f"}}
        }));
        OpenAIPreprocessor::normalize_thinking_arg(&mut request, Some("minimax_m3"), None);
        let args = request.chat_template_args.as_ref().unwrap();
        assert_eq!(
            args.get("thinking_mode"),
            Some(&serde_json::json!("disabled"))
        );
    }

    #[test]
    fn test_normalize_thinking_arg_m3_tool_call_only_deployment_gets_default() {
        // Tool-call-only M3 deployments still render the M3 template.
        // Guided-output requests therefore still need "adaptive" overridden.
        for tool_call_parser in [
            "minimax_m3",
            "minimax-m3",
            "minimax_m3_nom",
            "minimax-m3-nom",
        ] {
            let mut request = minimax_m3_request(serde_json::json!({
                "tools": [{"type": "function", "function": {"name": "f", "parameters": {"type": "object"}}}],
                "tool_choice": "required"
            }));
            OpenAIPreprocessor::normalize_thinking_arg(&mut request, None, Some(tool_call_parser));
            let args = request.chat_template_args.as_ref().unwrap();
            assert_eq!(
                args.get("thinking_mode"),
                Some(&serde_json::json!("disabled")),
                "tool_call_parser={tool_call_parser:?} + tool_choice=required with no reasoning parser must still default thinking_mode=disabled"
            );
        }
    }

    #[test]
    fn test_normalize_thinking_arg_m3_preserves_client_adaptive() {
        let mut request = minimax_m3_request(serde_json::json!({
            "response_format": {
                "type": "json_schema",
                "json_schema": {"name": "s", "schema": {"type": "object"}}
            },
            "chat_template_kwargs": {"thinking_mode": "adaptive"}
        }));
        OpenAIPreprocessor::normalize_thinking_arg(&mut request, Some("minimax_m3"), None);
        let args = request.chat_template_args.as_ref().unwrap();
        assert_eq!(
            args.get("thinking_mode"),
            Some(&serde_json::json!("adaptive"))
        );
        assert!(!args.contains_key("thinking"));
        assert!(!args.contains_key("enable_thinking"));
    }

    #[test]
    fn test_normalize_thinking_arg_m3_bridges_thinking_bool_to_mode() {
        let mut request = minimax_m3_request(serde_json::json!({
            "chat_template_kwargs": {"thinking": true}
        }));
        OpenAIPreprocessor::normalize_thinking_arg(&mut request, Some("minimax_m3"), None);
        let args = request.chat_template_args.as_ref().unwrap();
        assert_eq!(args.get("thinking"), Some(&serde_json::json!(true)));
        assert_eq!(args.get("enable_thinking"), Some(&serde_json::json!(true)));
        assert_eq!(
            args.get("thinking_mode"),
            Some(&serde_json::json!("enabled"))
        );
    }

    #[test]
    fn test_normalize_thinking_arg_m3_bridges_enable_thinking_to_mode() {
        let mut request = minimax_m3_request(serde_json::json!({
            "chat_template_kwargs": {"enable_thinking": false}
        }));
        OpenAIPreprocessor::normalize_thinking_arg(&mut request, Some("minimax_m3"), None);
        let args = request.chat_template_args.as_ref().unwrap();
        assert_eq!(args.get("thinking"), Some(&serde_json::json!(false)));
        assert_eq!(args.get("enable_thinking"), Some(&serde_json::json!(false)));
        assert_eq!(
            args.get("thinking_mode"),
            Some(&serde_json::json!("disabled"))
        );
    }

    #[test]
    fn test_normalize_thinking_arg_thinking_mode_enabled_is_thinking_on() {
        // `thinking_mode` strings must bypass `is_truthy`, which would map
        // "enabled" to false. M3 inputs are canonicalized to exact values.
        for value in ["enabled", "ENABLED", "Enabled"] {
            let mut request = minimax_m3_request(serde_json::json!({
                "chat_template_kwargs": {"thinking_mode": value}
            }));
            OpenAIPreprocessor::normalize_thinking_arg(&mut request, Some("minimax_m3"), None);
            let args = request.chat_template_args.as_ref().unwrap();
            assert_eq!(
                args.get("thinking"),
                Some(&serde_json::json!(true)),
                "thinking_mode={value:?} must map to thinking=true"
            );
            assert_eq!(args.get("enable_thinking"), Some(&serde_json::json!(true)));
            assert_eq!(
                args.get("thinking_mode"),
                Some(&serde_json::json!("enabled"))
            );
        }
    }

    #[test]
    fn test_normalize_thinking_arg_thinking_mode_disabled_is_thinking_off() {
        for value in ["disabled", "DISABLED"] {
            let mut request = minimax_m3_request(serde_json::json!({
                "chat_template_kwargs": {"thinking_mode": value}
            }));
            OpenAIPreprocessor::normalize_thinking_arg(&mut request, Some("minimax_m3"), None);
            let args = request.chat_template_args.as_ref().unwrap();
            assert_eq!(args.get("thinking"), Some(&serde_json::json!(false)));
            assert_eq!(args.get("enable_thinking"), Some(&serde_json::json!(false)));
            assert_eq!(
                args.get("thinking_mode"),
                Some(&serde_json::json!("disabled"))
            );
        }
    }

    #[test]
    fn test_normalize_thinking_arg_m3_canonicalizes_non_canonical_thinking_mode() {
        // M3 only treats exact "disabled" as off.
        // Canonicalize falsy aliases so `thinking=false` and the template gate agree.
        for value in [
            serde_json::json!(false),
            serde_json::json!(0),
            serde_json::json!("false"),
            serde_json::json!("no"),
            serde_json::json!("off"),
        ] {
            let mut request = minimax_m3_request(serde_json::json!({
                "chat_template_kwargs": {"thinking_mode": value.clone()}
            }));
            OpenAIPreprocessor::normalize_thinking_arg(&mut request, Some("minimax_m3"), None);
            let args = request.chat_template_args.as_ref().unwrap();
            assert_eq!(
                args.get("thinking_mode"),
                Some(&serde_json::json!("disabled")),
                "input={value} must canonicalize to \"disabled\""
            );
            assert_eq!(args.get("thinking"), Some(&serde_json::json!(false)));
            assert_eq!(args.get("enable_thinking"), Some(&serde_json::json!(false)));
        }

        // Truthy non-canonical values must canonicalize to "enabled".
        for value in [
            serde_json::json!(true),
            serde_json::json!(1),
            serde_json::json!("true"),
            serde_json::json!("yes"),
            serde_json::json!("on"),
        ] {
            let mut request = minimax_m3_request(serde_json::json!({
                "chat_template_kwargs": {"thinking_mode": value.clone()}
            }));
            OpenAIPreprocessor::normalize_thinking_arg(&mut request, Some("minimax_m3"), None);
            let args = request.chat_template_args.as_ref().unwrap();
            assert_eq!(
                args.get("thinking_mode"),
                Some(&serde_json::json!("enabled")),
                "input={value} must canonicalize to \"enabled\""
            );
            assert_eq!(args.get("thinking"), Some(&serde_json::json!(true)));
            assert_eq!(args.get("enable_thinking"), Some(&serde_json::json!(true)));
        }
    }

    #[test]
    fn test_normalize_thinking_arg_deepseek_thinking_mode_words_preserved() {
        // DeepSeek V3.1 uses `thinking_mode` values "chat" and "thinking".
        // Preserve them verbatim without writing boolean aliases.
        for value in ["thinking", "chat"] {
            let mut request: NvCreateChatCompletionRequest =
                serde_json::from_value(serde_json::json!({
                    "model": "deepseek/DeepSeek-V3.1",
                    "messages": [{"role": "user", "content": "hi"}],
                    "chat_template_kwargs": {"thinking_mode": value}
                }))
                .unwrap();
            OpenAIPreprocessor::normalize_thinking_arg(&mut request, Some("deepseek_v3_1"), None);
            let args = request.chat_template_args.as_ref().unwrap();
            assert_eq!(
                args.get("thinking_mode").and_then(|v| v.as_str()),
                Some(value),
                "DeepSeek {value:?} must survive verbatim"
            );
            assert!(
                !args.contains_key("thinking"),
                "no boolean coercion for DeepSeek {value:?}"
            );
            assert!(!args.contains_key("enable_thinking"));
        }
    }

    #[test]
    fn test_normalize_thinking_arg_thinking_mode_not_written_for_non_m3_parsers() {
        // Only M3-family templates use the "enabled"/"disabled" vocabulary.
        // Other parsers may treat an injected value as out-of-vocabulary.
        for parser in [
            "deepseek_v3_1",
            "deepseek_v3_2",
            "minimax_m2",
            "qwen3",
            "kimi_k25",
            "kimi_k3",
        ] {
            let mut request: NvCreateChatCompletionRequest =
                serde_json::from_value(serde_json::json!({
                    "model": "test",
                    "messages": [{"role": "user", "content": "hi"}],
                    "chat_template_kwargs": {"thinking": true}
                }))
                .unwrap();
            OpenAIPreprocessor::normalize_thinking_arg(&mut request, Some(parser), None);
            let args = request.chat_template_args.as_ref().unwrap();
            assert_eq!(args.get("thinking"), Some(&serde_json::json!(true)));
            assert_eq!(args.get("enable_thinking"), Some(&serde_json::json!(true)));
            assert!(
                !args.contains_key("thinking_mode"),
                "parser {parser:?} must not receive an injected thinking_mode value"
            );
        }
    }

    #[test]
    fn test_normalize_thinking_arg_kimi_or_else_does_not_write_thinking_mode() {
        // Kimi parsers synthesize `Some(true)` with no client input.
        // Write boolean aliases only; Kimi does not read `thinking_mode`.
        for parser in ["kimi_k25", "kimi_k3", "kimi-k3"] {
            let mut request: NvCreateChatCompletionRequest =
                serde_json::from_value(serde_json::json!({
                    "model": "test",
                    "messages": [{"role": "user", "content": "hi"}]
                }))
                .unwrap();
            OpenAIPreprocessor::normalize_thinking_arg(&mut request, Some(parser), None);
            let args = request.chat_template_args.as_ref().unwrap();
            assert_eq!(args.get("thinking"), Some(&serde_json::json!(true)));
            assert!(!args.contains_key("thinking_mode"));
        }
    }

    #[test]
    fn test_normalize_thinking_arg_m3_leaves_plain_chat_untouched() {
        let mut request = minimax_m3_request(serde_json::json!({}));
        OpenAIPreprocessor::normalize_thinking_arg(&mut request, Some("minimax_m3"), None);
        assert!(request.chat_template_args.is_none());
    }

    #[test]
    fn test_normalize_thinking_arg_non_m3_adaptive_is_no_op() {
        // Preserve non-M3 `thinking_mode: "adaptive"` verbatim by leaving
        // normalization as `None`.
        let mut request: NvCreateChatCompletionRequest =
            serde_json::from_value(serde_json::json!({
                "model": "test",
                "messages": [{"role": "user", "content": "hi"}],
                "chat_template_kwargs": {"thinking_mode": "adaptive"}
            }))
            .unwrap();
        OpenAIPreprocessor::normalize_thinking_arg(&mut request, Some("qwen3"), None);
        let args = request.chat_template_args.as_ref().unwrap();
        assert_eq!(
            args.get("thinking_mode"),
            Some(&serde_json::json!("adaptive"))
        );
        assert!(!args.contains_key("thinking"));
        assert!(!args.contains_key("enable_thinking"));
    }

    #[test]
    fn test_normalize_thinking_arg_operator_default_forces_m3_constrained_disabled() {
        // Runtime defaults are operator policy, not client intent. When the
        // client sends no thinking control, constrained M3 requests force
        // "disabled" even if the operator default is "enabled" — otherwise
        // the deployment default silently propagates a mode M3 cannot honor
        // with a constrained decoder attached.
        let runtime_config = runtime_config_with_default_thinking_mode("enabled");
        let mut request = minimax_m3_request(serde_json::json!({
            "response_format": {
                "type": "json_schema",
                "json_schema": {"name": "s", "schema": {"type": "object"}}
            }
        }));
        let original_args = request.chat_template_args.clone();
        assert!(OpenAIPreprocessor::request_disables_reasoning_with_family(
            &request,
            None,
            Some("minimax_m3"),
            Some("enabled"),
            Some("minimax_m3"),
        ));
        assert_eq!(request.chat_template_args, original_args);
        let thinking_control_from_client =
            OpenAIPreprocessor::request_has_client_thinking_control(&request);
        assert!(
            !thinking_control_from_client,
            "no chat_template_kwargs → not client intent"
        );
        OpenAIPreprocessor::apply_default_thinking_mode_from_runtime_config(
            &runtime_config,
            &mut request,
        );
        OpenAIPreprocessor::normalize_thinking_arg_with_source(
            &mut request,
            Some("minimax_m3"),
            None,
            thinking_control_from_client,
        );
        let args = request.chat_template_args.as_ref().unwrap();
        assert_eq!(
            args.get("thinking_mode"),
            Some(&serde_json::json!("disabled"))
        );
        assert_eq!(args.get("thinking"), Some(&serde_json::json!(false)));
        assert_eq!(args.get("enable_thinking"), Some(&serde_json::json!(false)));
    }

    #[test]
    fn test_normalize_thinking_arg_m3_preserves_explicit_thinking_for_constrained() {
        // Explicit client thinking intent is respected for constrained M3
        // requests. The runtime attempts the combination even though the
        // template's adaptive/enabled path is known to conflict with the
        // constrained decoder — this is the client's opt-in, and returning
        // possibly-degraded content is preferred to a hard reject at the
        // API boundary (which would break provider-compatibility probes
        // that expect the request to be attempted).
        for (body, expected_mode, desc) in [
            (
                serde_json::json!({
                    "response_format": {
                        "type": "json_schema",
                        "json_schema": {"name": "s", "schema": {"type": "object"}}
                    },
                    "chat_template_kwargs": {"thinking_mode": "enabled"}
                }),
                "enabled",
                "json_schema + explicit thinking_mode=enabled",
            ),
            (
                serde_json::json!({
                    "tools": [{"type": "function", "function": {"name": "f", "parameters": {"type": "object"}}}],
                    "tool_choice": "required",
                    "chat_template_kwargs": {"thinking_mode": "adaptive"}
                }),
                "adaptive",
                "required tool + explicit thinking_mode=adaptive",
            ),
            (
                serde_json::json!({
                    "tools": [{"type": "function", "function": {"name": "f", "parameters": {"type": "object"}}}],
                    "tool_choice": {"type": "function", "function": {"name": "f"}},
                    "chat_template_kwargs": {"thinking": true}
                }),
                "enabled",
                "named tool + explicit thinking=true",
            ),
        ] {
            let mut request = minimax_m3_request(body);
            assert!(!OpenAIPreprocessor::request_disables_reasoning_with_family(
                &request,
                None,
                Some("minimax_m3"),
                Some("disabled"),
                Some("minimax_m3"),
            ));
            let thinking_control_from_client =
                OpenAIPreprocessor::request_has_client_thinking_control(&request);
            assert!(
                thinking_control_from_client,
                "{desc} must be classified as client thinking intent"
            );
            OpenAIPreprocessor::normalize_thinking_arg_with_source(
                &mut request,
                Some("minimax_m3"),
                None,
                thinking_control_from_client,
            );
            let args = request.chat_template_args.as_ref().unwrap();
            assert_eq!(
                args.get("thinking_mode"),
                Some(&serde_json::json!(expected_mode)),
                "{desc} must preserve explicit client thinking"
            );
        }
    }

    /// PRE.2 — Per-request reasoning gate. See `lib/llm/PREPROCESSOR_CASES.md`.
    #[test]
    fn test_is_reasoning_disabled_by_request() {
        let thinking_true = {
            let mut m = std::collections::HashMap::new();
            m.insert("thinking".to_string(), serde_json::Value::Bool(true));
            m
        };
        let thinking_false = {
            let mut m = std::collections::HashMap::new();
            m.insert("thinking".to_string(), serde_json::Value::Bool(false));
            m
        };
        let enable_thinking_true = {
            let mut m = std::collections::HashMap::new();
            m.insert("enable_thinking".to_string(), serde_json::Value::Bool(true));
            m
        };
        let enable_thinking_false = {
            let mut m = std::collections::HashMap::new();
            m.insert(
                "enable_thinking".to_string(),
                serde_json::Value::Bool(false),
            );
            m
        };
        let force_nonempty_content_true = {
            let mut m = std::collections::HashMap::new();
            m.insert(
                "force_nonempty_content".to_string(),
                serde_json::Value::Bool(true),
            );
            m
        };
        let thinking_mode_chat = {
            let mut m = std::collections::HashMap::new();
            m.insert(
                "thinking_mode".to_string(),
                serde_json::Value::String("chat".to_string()),
            );
            m
        };
        let thinking_mode_thinking = {
            let mut m = std::collections::HashMap::new();
            m.insert(
                "thinking_mode".to_string(),
                serde_json::Value::String("thinking".to_string()),
            );
            m
        };
        let thinking_mode_disabled = {
            let mut m = std::collections::HashMap::new();
            m.insert(
                "thinking_mode".to_string(),
                serde_json::Value::String("disabled".to_string()),
            );
            m
        };
        let reasoning_effort_none = {
            let mut m = std::collections::HashMap::new();
            m.insert(
                "reasoning_effort".to_string(),
                serde_json::Value::String("none".to_string()),
            );
            m
        };
        let reasoning_effort_high = {
            let mut m = std::collections::HashMap::new();
            m.insert(
                "reasoning_effort".to_string(),
                serde_json::Value::String("high".to_string()),
            );
            m
        };
        let empty_args = std::collections::HashMap::new();

        // (parser, args, expected_disabled, description)
        let cases = [
            (
                Some("kimi_k25"),
                Some(&thinking_false),
                true,
                "kimi_k25 + thinking=false → disabled",
            ),
            (
                Some("kimi_k25"),
                Some(&thinking_true),
                false,
                "kimi_k25 + thinking=true → enabled",
            ),
            (
                Some("kimi_k25"),
                None,
                false,
                "kimi_k25 + no args → enabled",
            ),
            (
                Some("kimi_k25"),
                Some(&empty_args),
                false,
                "kimi_k25 + empty args → enabled",
            ),
            (
                Some("kimi_k3"),
                Some(&thinking_false),
                true,
                "kimi_k3 + thinking=false → disabled",
            ),
            (
                Some("kimi-k3"),
                Some(&thinking_true),
                false,
                "kimi-k3 + thinking=true → enabled",
            ),
            // deepseek_r1 uses "thinking" bool or "thinking_mode" string
            (
                Some("deepseek_r1"),
                Some(&thinking_false),
                true,
                "deepseek_r1 + thinking=false → disabled",
            ),
            (
                Some("deepseek_r1"),
                Some(&thinking_true),
                false,
                "deepseek_r1 + thinking=true → enabled",
            ),
            (
                Some("deepseek_r1"),
                Some(&thinking_mode_chat),
                true,
                "deepseek_r1 + thinking_mode=chat → disabled",
            ),
            (
                Some("deepseek_r1"),
                Some(&thinking_mode_thinking),
                false,
                "deepseek_r1 + thinking_mode=thinking → enabled",
            ),
            (
                Some("deepseek_r1"),
                None,
                false,
                "deepseek_r1 + no args → enabled",
            ),
            (
                Some("deepseek_r1"),
                Some(&empty_args),
                false,
                "deepseek_r1 + empty args → enabled",
            ),
            (
                Some("deepseek_v3"),
                None,
                true,
                "deepseek_v3 + no args → disabled",
            ),
            (
                Some("deepseek_v3"),
                Some(&thinking_true),
                false,
                "deepseek_v3 + thinking=true → enabled",
            ),
            (
                Some("deepseek_v3_1"),
                Some(&enable_thinking_true),
                false,
                "deepseek_v3_1 + enable_thinking=true → enabled",
            ),
            (
                Some("deepseek_v3_2"),
                None,
                false,
                "deepseek_v3_2 + no args → enabled",
            ),
            (
                Some("deepseek_v3_2"),
                Some(&thinking_false),
                true,
                "deepseek_v3_2 + thinking=false → disabled",
            ),
            (
                Some("minimax_m2"),
                Some(&thinking_false),
                true,
                "minimax_m2 + thinking=false → disabled",
            ),
            (
                Some("minimax_m2"),
                Some(&thinking_true),
                false,
                "minimax_m2 + thinking=true → enabled",
            ),
            (
                Some("minimax_m2"),
                None,
                false,
                "minimax_m2 + no args → enabled",
            ),
            (
                Some("basic"),
                Some(&thinking_false),
                false,
                "basic → never disabled",
            ),
            (
                None,
                Some(&thinking_false),
                false,
                "no parser → never disabled",
            ),
            // nemotron_nano uses "enable_thinking" key
            (
                Some("nemotron_nano"),
                Some(&enable_thinking_false),
                true,
                "nemotron_nano + enable_thinking=false → disabled",
            ),
            (
                Some("nemotron_nano"),
                Some(&enable_thinking_true),
                false,
                "nemotron_nano + enable_thinking=true → enabled",
            ),
            (
                Some("nemotron_nano"),
                None,
                false,
                "nemotron_nano + no args → enabled",
            ),
            (
                Some("nemotron_nano"),
                Some(&empty_args),
                false,
                "nemotron_nano + empty args → enabled",
            ),
            (
                Some("nemotron3"),
                Some(&force_nonempty_content_true),
                false,
                "nemotron3 + force_nonempty_content=true → NOT disabled (parser stays on)",
            ),
            (
                Some("nemotron_v3"),
                Some(&enable_thinking_false),
                true,
                "nemotron_v3 + enable_thinking=false → disabled",
            ),
            (
                Some("nemotron_v3"),
                Some(&force_nonempty_content_true),
                false,
                "nemotron_v3 + force_nonempty_content=true → NOT disabled (parser stays on)",
            ),
            // deepseek_v4 — same convention as deepseek_r1; verify all three aliases
            // (deepseek_v4 / deepseek-v4 / deepseekv4) plus both signal keys.
            (
                Some("deepseek_v4"),
                Some(&thinking_false),
                true,
                "deepseek_v4 + thinking=false → disabled",
            ),
            (
                Some("deepseek_v4"),
                Some(&thinking_true),
                false,
                "deepseek_v4 + thinking=true → enabled",
            ),
            (
                Some("deepseek_v4"),
                Some(&thinking_mode_chat),
                true,
                "deepseek_v4 + thinking_mode=chat → disabled",
            ),
            (
                Some("deepseek_v4"),
                Some(&thinking_mode_thinking),
                false,
                "deepseek_v4 + thinking_mode=thinking → enabled",
            ),
            (
                Some("deepseek_v4"),
                None,
                false,
                "deepseek_v4 + no args → enabled",
            ),
            (
                Some("deepseek-v4"),
                Some(&thinking_false),
                true,
                "deepseek-v4 (hyphen alias) + thinking=false → disabled",
            ),
            (
                Some("deepseekv4"),
                Some(&thinking_mode_chat),
                true,
                "deepseekv4 (joined alias) + thinking_mode=chat → disabled",
            ),
            (
                Some("deepseek_v4"),
                Some(&enable_thinking_false),
                true,
                "deepseek_v4 + enable_thinking=false → disabled (vLLM alias)",
            ),
            (
                Some("deepseek_v4"),
                Some(&enable_thinking_true),
                false,
                "deepseek_v4 + enable_thinking=true → enabled (vLLM alias)",
            ),
            (
                Some("gemma4"),
                Some(&enable_thinking_false),
                true,
                "gemma4 + enable_thinking=false → disabled",
            ),
            (
                Some("gemma4"),
                Some(&enable_thinking_true),
                false,
                "gemma4 + enable_thinking=true → enabled",
            ),
            (
                Some("gemma4"),
                None,
                true,
                "gemma4 + no args → disabled (reasoning is opt-in)",
            ),
            (
                Some("gemma-4"),
                Some(&enable_thinking_false),
                true,
                "gemma-4 (hyphen alias) + enable_thinking=false → disabled",
            ),
            (
                Some("gemma-4"),
                None,
                true,
                "gemma-4 (hyphen alias) + no args → disabled (reasoning is opt-in)",
            ),
            (Some("mistral"), None, true, "mistral + no args → disabled"),
            (
                Some("mistral"),
                Some(&reasoning_effort_none),
                true,
                "mistral + reasoning_effort=none → disabled",
            ),
            (
                Some("mistral"),
                Some(&reasoning_effort_high),
                false,
                "mistral + reasoning_effort=high → enabled",
            ),
            (
                Some("minimax_m3"),
                Some(&thinking_mode_disabled),
                true,
                "minimax_m3 + thinking_mode=disabled → disabled",
            ),
            (
                Some("minimax-m3"),
                Some(&thinking_mode_disabled),
                true,
                "minimax-m3 + thinking_mode=disabled → disabled",
            ),
            (
                Some("minimax_m3"),
                Some(&thinking_mode_thinking),
                false,
                "minimax_m3 + thinking_mode=thinking → enabled",
            ),
            (
                Some("minimax_m3"),
                None,
                false,
                "minimax_m3 + no args → enabled",
            ),
        ];

        // The disable decision no longer depends on streaming — `enable_thinking`
        // and the per-family signals decide it identically for both paths.
        for (parser, args, expected, desc) in cases {
            assert_eq!(
                OpenAIPreprocessor::is_reasoning_disabled_by_request(parser, args),
                expected,
                "FAILED: {desc}",
            );
        }

        // force_nonempty_content=true does NOT disable reasoning parsing for
        // either path: the parser stays on so reasoning is split from the answer
        // (no leak). Non-streaming additionally moves reasoning into content when
        // no content was generated (the aggregator); streaming skips that move.
        assert!(
            !OpenAIPreprocessor::is_reasoning_disabled_by_request(
                Some("nemotron3"),
                Some(&force_nonempty_content_true),
            ),
            "nemotron3 + force_nonempty_content=true → NOT disabled",
        );
        assert!(
            !OpenAIPreprocessor::is_reasoning_disabled_by_request(
                Some("nemotron_v3"),
                Some(&force_nonempty_content_true),
            ),
            "nemotron_v3 + force_nonempty_content=true → NOT disabled",
        );
        // enable_thinking=false disables entirely (user turned thinking off).
        assert!(
            OpenAIPreprocessor::is_reasoning_disabled_by_request(
                Some("nemotron3"),
                Some(&enable_thinking_false),
            ),
            "nemotron3 + enable_thinking=false → disabled",
        );

        // The force_nonempty_content=true → NOT disabled behavior is what lets a
        // reasoning-only non-streaming turn surface reasoning as content; verify
        // the aggregator half in test_move_reasoning_to_content_when_empty.
    }

    #[test]
    fn muse_force_nonempty_stream_reports_that_it_can_defer_all_output() {
        let mut args = std::collections::HashMap::new();
        args.insert(
            "force_nonempty_content".to_string(),
            serde_json::Value::Bool(true),
        );

        assert!(OpenAIPreprocessor::stream_can_defer_all_output(
            None,
            Some("muse_glimmer"),
            Some(&args),
        ));
    }

    /// Different query strings must produce different hashes. `?v=1` and
    /// `?v=2` may look like cache-busters, but they could equally be a
    /// content selector ("version 2 of the image"). The URL alone doesn't
    /// tell us which, so we keep the hash URL-identical and let the URL be
    /// the identity. For signed-URL workloads where rotation actually
    /// hides a stable object, `--frontend-decoding` hashes the decoded
    /// bytes instead.
    #[cfg(feature = "mm-routing")]
    #[test]
    fn hash_image_url_distinguishes_query_strings() {
        let base = "https://cdn.example.com/img.jpg";
        let v1 = OpenAIPreprocessor::hash_image_url(&format!("{base}?v=1"));
        let v2 = OpenAIPreprocessor::hash_image_url(&format!("{base}?v=2"));
        let no_q = OpenAIPreprocessor::hash_image_url(base);
        assert_ne!(v1, v2, "different query values must hash differently");
        assert_ne!(v1, no_q, "presence of a query string must change the hash");
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn image_dim_cache_preserves_terminal_policy_classification() {
        let terminal = ImageDimFetchFailure::from_error(invalid_argument_error(
            "media destination rejected by SSRF policy",
        ));
        let terminal = terminal.to_error();
        assert!(MediaFetcher::is_policy_rejection(&terminal));

        let recoverable =
            ImageDimFetchFailure::from_error(anyhow::anyhow!("image header was truncated"));
        let recoverable = recoverable.to_error();
        assert!(!MediaFetcher::is_policy_rejection(&recoverable));
    }

    /// The gather loop applies the workers' data: URL cap on the path that
    /// passes URLs through to the backend, where no worker check runs first.
    #[tokio::test]
    async fn gather_rejects_data_url_over_the_size_cap() {
        let mdc = ModelDeploymentCard::load_from_disk(
            "tests/data/sample-models/mock-llama-3.1-8b-instruct",
            None,
        )
        .unwrap();
        let preprocessor = OpenAIPreprocessor::new(mdc).unwrap();
        assert!(preprocessor.media_loader.is_none());

        let request_with_data_url = |size: usize| -> NvCreateChatCompletionRequest {
            let prefix = "data:audio/wav;base64,";
            let url = format!("{prefix}{}", "A".repeat(size - prefix.len()));
            serde_json::from_value(serde_json::json!({
                "model": "test-model",
                "messages": [{
                    "role": "user",
                    "content": [{"type": "audio_url", "audio_url": {"url": url}}]
                }]
            }))
            .unwrap()
        };

        temp_env::async_with_vars([("DYN_MM_MAX_DATA_URL_MB", Some("1"))], async {
            let limit = 1024 * 1024;
            let mut builder = PreprocessedRequest::builder();
            preprocessor
                .gather_multi_modal_data(&request_with_data_url(limit), &mut builder, None, &[])
                .await
                .expect("a data: URL at the cap is accepted");

            let mut builder = PreprocessedRequest::builder();
            let error = preprocessor
                .gather_multi_modal_data(&request_with_data_url(limit + 1), &mut builder, None, &[])
                .await
                .expect_err("a data: URL over the cap is rejected");
            let dynamo_error = error
                .downcast_ref::<DynamoError>()
                .expect("error should preserve the DynamoError type");
            assert_eq!(dynamo_error.error_type(), ErrorType::InvalidArgument);
            assert!(
                dynamo_error.message().contains(
                    "audio_url data: URL is 1048577 bytes, exceeds the 1048576-byte limit"
                ),
                "{}",
                dynamo_error.message()
            );
            assert!(
                dynamo_error.message().contains(
                    "To raise the limit, set DYN_MM_MAX_DATA_URL_MB (in megabytes) on both \
                     the frontend and the workers."
                ),
                "{}",
                dynamo_error.message()
            );
            // The 400 body carries the same text.
            assert_eq!(dynamo_error.public_message(), Some(dynamo_error.message()));
        })
        .await;
    }

    /// A blocked destination on the URL-passthrough path must fail the whole
    /// request. The IP literal is refused before DNS, so no socket is opened.
    #[cfg(feature = "mm-routing")]
    #[tokio::test]
    async fn url_passthrough_policy_rejection_is_terminal() {
        // The probe's fetcher is built from the environment. The opt-in allows
        // this destination, so there would be nothing to assert.
        if std::env::var("DYN_MM_ALLOW_INTERNAL").as_deref() == Ok("1") {
            return;
        }

        let mdc = ModelDeploymentCard::load_from_disk(
            "tests/data/sample-models/mock-llama-3.1-8b-instruct",
            None,
        )
        .unwrap();
        let preprocessor = OpenAIPreprocessor::new(mdc).unwrap();
        assert!(preprocessor.media_loader.is_none());

        let request: NvCreateChatCompletionRequest = serde_json::from_value(serde_json::json!({
            "model": "test-model",
            "messages": [{
                "role": "user",
                "content": [{
                    "type": "image_url",
                    "image_url": {
                        "url": "https://169.254.169.254/latest/meta-data/private-image.png"
                    }
                }]
            }]
        }))
        .unwrap();
        let mut builder = PreprocessedRequest::builder();

        let error = preprocessor
            .gather_multi_modal_data(&request, &mut builder, None, &[])
            .await
            .expect_err("URL-passthrough must stop at the policy rejection");

        assert!(MediaFetcher::is_policy_rejection(&error));
    }

    /// Object-store and file URLs are backend-owned passthrough schemes, so a
    /// failed dimension probe must remain recoverable rather than become 4xx.
    #[cfg(feature = "mm-routing")]
    #[tokio::test]
    async fn dim_fetch_declines_passthrough_schemes_without_rejecting_them() {
        for url in [
            "s3://bucket/private-image.png",
            "gs://bucket/private-image.png",
            "file:///tmp/private-image.png",
        ] {
            let error = OpenAIPreprocessor::fetch_image_dims_uncached(
                url,
                RoutingImageDimensionPolicy::Encoded,
            )
            .await
            .expect_err("a non-fetchable scheme cannot yield dimensions");
            assert!(
                !MediaFetcher::is_policy_rejection(&error),
                "{url} must be a skippable dim-fetch failure, not a terminal 4xx: {error:#}"
            );
        }
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn exif_dimension_policy_is_limited_to_kimi_k3_vllm_url_passthrough() {
        use crate::local_model::runtime_config::{
            ModelRuntimeConfig, SGLANG_GENERATE_CAPABILITY, VLLM_INFERENCE_V1_GENERATE_CAPABILITY,
        };

        let kimi_k3 = Some(RoutingImagePromptLayout::KimiK3 {
            media_begin: 1,
            media_content: 2,
            media_end: 3,
        });
        let mut runtime_config = ModelRuntimeConfig::default();
        assert_eq!(
            routing_image_dimension_policy(&runtime_config, false, kimi_k3),
            RoutingImageDimensionPolicy::Encoded
        );

        let mut sglang_config = ModelRuntimeConfig::default();
        sglang_config
            .set_engine_specific(SGLANG_GENERATE_CAPABILITY, true)
            .unwrap();
        assert_eq!(
            routing_image_dimension_policy(&sglang_config, false, kimi_k3),
            RoutingImageDimensionPolicy::Encoded
        );

        runtime_config
            .set_engine_specific(VLLM_INFERENCE_V1_GENERATE_CAPABILITY, true)
            .unwrap();
        assert_eq!(
            routing_image_dimension_policy(&runtime_config, false, kimi_k3),
            RoutingImageDimensionPolicy::ExifTransposed
        );
        assert_eq!(
            routing_image_dimension_policy(&runtime_config, true, kimi_k3),
            RoutingImageDimensionPolicy::Encoded
        );
        assert_eq!(
            routing_image_dimension_policy(
                &runtime_config,
                false,
                Some(RoutingImagePromptLayout::RepeatedPad),
            ),
            RoutingImageDimensionPolicy::Encoded
        );
    }

    #[cfg(all(feature = "mm-routing", feature = "media-ffmpeg"))]
    #[test]
    fn dual_qwen_video_contracts_disable_exact_routing() {
        let mut runtime_config = ModelRuntimeConfig::default();
        runtime_config
            .set_engine_specific(
                VLLM_QWEN_VIDEO_PROCESSOR_CONTRACT_RUNTIME_KEY,
                serde_json::json!({
                    "placeholder_target": "bare_video_token",
                    "resize_mode": "legacy_ceil"
                }),
            )
            .unwrap();
        runtime_config
            .set_engine_specific(
                SGLANG_QWEN_VIDEO_PROCESSOR_CONTRACT_RUNTIME_KEY,
                serde_json::json!({
                    "placeholder_target": "bare_video_token",
                    "resize_mode": "legacy_ceil",
                    "runless_boundary_hash": "tokens_only",
                    "sglang_preprocess": {
                        "image_factor": 28,
                        "video_min_pixels": 100352,
                        "video_max_pixels": 602112,
                        "video_total_pixels": 90316800,
                        "frame_factor": 2,
                        "fps": 2.0,
                        "min_frames": 4,
                        "max_frames": 768
                    }
                }),
            )
            .unwrap();

        let error = resolve_qwen_video_processor_contract(&runtime_config).unwrap_err();
        assert!(
            error
                .to_string()
                .contains("multiple Qwen video processor contracts")
        );
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn exif_transposed_dimensions_match_vllm_image_loading() {
        use image::{ExtendedColorType, ImageEncoder, codecs::jpeg::JpegEncoder};

        // Little-endian TIFF with one orientation entry set to 6 (rotate 90°).
        let exif = vec![
            b'I', b'I', 42, 0, 8, 0, 0, 0, 1, 0, 0x12, 0x01, 3, 0, 1, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0,
            0,
        ];
        let mut jpeg = Vec::new();
        let mut encoder = JpegEncoder::new(&mut jpeg);
        encoder.set_exif_metadata(exif).unwrap();
        encoder
            .encode(&[255, 0, 0, 0, 255, 0], 2, 1, ExtendedColorType::Rgb8)
            .unwrap();

        assert_eq!(
            OpenAIPreprocessor::dimensions_from_image_bytes(
                &jpeg,
                RoutingImageDimensionPolicy::Encoded,
            )
            .unwrap(),
            (2, 1)
        );
        assert_eq!(
            OpenAIPreprocessor::dimensions_from_image_bytes(
                &jpeg,
                RoutingImageDimensionPolicy::ExifTransposed,
            )
            .unwrap(),
            (1, 2)
        );
    }

    /// Rotating S3 / GCS / Azure SAS signatures change the URL and
    /// therefore the hash. This is a known limitation of URL-passthrough
    /// routing for signed-URL workloads — `--frontend-decoding` is the
    /// recommended mode there because it hashes the decoded image bytes
    /// regardless of how the URL was signed.
    #[cfg(feature = "mm-routing")]
    #[test]
    fn hash_image_url_distinguishes_rotating_signatures() {
        let base = "https://bucket.s3.amazonaws.com/img.jpg";
        let a = OpenAIPreprocessor::hash_image_url(&format!(
            "{base}?X-Amz-Signature=AAA&X-Amz-Date=20260101T000000Z&X-Amz-Expires=600"
        ));
        let b = OpenAIPreprocessor::hash_image_url(&format!(
            "{base}?X-Amz-Signature=BBB&X-Amz-Date=20260101T010000Z&X-Amz-Expires=900"
        ));
        assert_ne!(
            a, b,
            "rotating presign params produce a different URL and must hash differently"
        );
    }

    /// Identical URLs must hash to the same value (the basic identity
    /// guarantee that makes URL-passthrough routing useful at all).
    #[cfg(feature = "mm-routing")]
    #[test]
    fn hash_image_url_is_deterministic_for_identical_urls() {
        let url = "https://cdn.example.com/img.jpg?width=256";
        assert_eq!(
            OpenAIPreprocessor::hash_image_url(url),
            OpenAIPreprocessor::hash_image_url(url),
        );
    }

    /// data: URIs hash the entire URI string. Same payload → same hash;
    /// different payload → different hash.
    #[cfg(feature = "mm-routing")]
    #[test]
    fn hash_image_url_data_uri_content_addressed() {
        let same = "data:image/png;base64,AAAA";
        let other = "data:image/png;base64,BBBB";
        assert_eq!(
            OpenAIPreprocessor::hash_image_url(same),
            OpenAIPreprocessor::hash_image_url(same)
        );
        assert_ne!(
            OpenAIPreprocessor::hash_image_url(same),
            OpenAIPreprocessor::hash_image_url(other),
            "different data URI payloads must hash differently"
        );
    }

    /// Non-HTTP / non-data schemes (s3://, gs://, file://) hash as-is.
    #[cfg(feature = "mm-routing")]
    #[test]
    fn hash_image_url_other_schemes_passthrough() {
        let s3a = OpenAIPreprocessor::hash_image_url("s3://bucket/key?v=1");
        let s3b = OpenAIPreprocessor::hash_image_url("s3://bucket/key?v=2");
        assert_ne!(
            s3a, s3b,
            "s3:// query params identify objects and must not collide"
        );
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn tracked_video_boundary_uses_native_metadata_only_when_needed() {
        use dynamo_kv_router::protocols::pad_value_for_mm_hash;

        let video_token_id = 100;
        let mm_hash = 41;
        let video_pad = pad_value_for_mm_hash(mm_hash);
        let replacement = TrackedMmRoutingReplacement {
            mm_hash,
            target_tokens: vec![9],
            worker_tokens: vec![3, 4, 5, 6, video_token_id, video_token_id],
            routing_tokens: vec![3, 4, 5, 6, video_pad, video_pad],
        };

        let (tokens, prompt_len, infos) = apply_tracked_mm_replacements(
            None,
            &[replacement],
            &[1, 9, 2],
            4,
            Some(99),
            Some(video_token_id),
            mm_routing::KvEventMmIdentity::MmMetadata,
        )
        .unwrap();

        assert_eq!(prompt_len, 8);
        assert_eq!(&tokens[..4], &[1, 3, 4, 5]);
        assert_eq!(&tokens[4..], &[6, video_pad, video_pad, 2]);
        assert_eq!(infos[0].as_ref().unwrap().mm_objects[0].mm_hash, mm_hash);
        assert!(infos[1].is_none());
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn tracked_video_boundary_matches_token_only_worker_contract() {
        use dynamo_kv_router::protocols::pad_value_for_mm_hash;

        let video_token_id = 100;
        let mm_hash = 41;
        let video_pad = pad_value_for_mm_hash(mm_hash);
        let replacement = TrackedMmRoutingReplacement {
            mm_hash,
            target_tokens: vec![9],
            worker_tokens: vec![3, 4, 5, 6, video_token_id, video_token_id],
            routing_tokens: vec![3, 4, 5, 6, video_pad, video_pad],
        };

        let (tokens, prompt_len, infos) = apply_tracked_mm_replacements(
            None,
            &[replacement],
            &[1, 9, 2],
            4,
            Some(99),
            Some(video_token_id),
            mm_routing::KvEventMmIdentity::PadValueTokens,
        )
        .unwrap();

        assert_eq!(prompt_len, 8);
        assert_eq!(&tokens[..4], &[1, 3, 4, 5]);
        assert_eq!(&tokens[4..], &[6, video_pad, video_pad, 2]);
        assert!(infos.iter().all(Option::is_none));
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn tracked_token_only_worker_still_validates_normalizable_blocks() {
        let video_token_id = 100;
        let replacement = TrackedMmRoutingReplacement {
            mm_hash: 41,
            target_tokens: vec![9],
            worker_tokens: vec![video_token_id, video_token_id],
            routing_tokens: vec![1, 2],
        };

        let error = apply_tracked_mm_replacements(
            None,
            &[replacement],
            &[9],
            4,
            Some(99),
            Some(video_token_id),
            mm_routing::KvEventMmIdentity::PadValueTokens,
        )
        .unwrap_err();

        assert!(
            error
                .to_string()
                .contains("frontend MM replacement differs from KV-event normalization")
        );
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn tracked_mixed_boundary_preserves_worker_hash_fallback() {
        use dynamo_kv_router::protocols::pad_value_for_mm_hash;

        let image_token_id = 99;
        let video_token_id = 100;
        let image_hash = 41;
        let video_hash = 42;
        let replacements = [
            TrackedMmRoutingReplacement {
                mm_hash: image_hash,
                target_tokens: vec![image_token_id],
                worker_tokens: vec![image_token_id, image_token_id, 7],
                routing_tokens: vec![
                    pad_value_for_mm_hash(image_hash),
                    pad_value_for_mm_hash(image_hash),
                    7,
                ],
            },
            TrackedMmRoutingReplacement {
                mm_hash: video_hash,
                target_tokens: vec![video_token_id],
                worker_tokens: vec![8, video_token_id, video_token_id, 9],
                routing_tokens: vec![
                    8,
                    pad_value_for_mm_hash(video_hash),
                    pad_value_for_mm_hash(video_hash),
                    9,
                ],
            },
        ];

        let (tokens, prompt_len, infos) = apply_tracked_mm_replacements(
            None,
            &replacements,
            &[image_token_id, video_token_id],
            4,
            Some(image_token_id),
            Some(video_token_id),
            mm_routing::KvEventMmIdentity::MmMetadata,
        )
        .unwrap();

        assert_eq!(prompt_len, 7);
        assert_eq!(&tokens[..4], &[image_token_id, image_token_id, 7, 8]);
        assert_eq!(
            &tokens[4..],
            &[
                pad_value_for_mm_hash(video_hash),
                pad_value_for_mm_hash(video_hash),
                9,
                0
            ]
        );
        assert_eq!(
            infos[0]
                .as_ref()
                .unwrap()
                .mm_objects
                .iter()
                .map(|object| object.mm_hash)
                .collect::<Vec<_>>(),
            [image_hash, video_hash]
        );
        assert!(infos[1].is_none());
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn tracked_mixed_token_only_boundary_preserves_canonical_pads() {
        use dynamo_kv_router::protocols::pad_value_for_mm_hash;

        let image_token_id = 99;
        let video_token_id = 100;
        let image_hash = 41;
        let video_hash = 42;
        let image_pad = pad_value_for_mm_hash(image_hash);
        let video_pad = pad_value_for_mm_hash(video_hash);
        let replacements = [
            TrackedMmRoutingReplacement {
                mm_hash: image_hash,
                target_tokens: vec![image_token_id],
                worker_tokens: vec![image_token_id; 14],
                routing_tokens: vec![image_pad; 14],
            },
            TrackedMmRoutingReplacement {
                mm_hash: video_hash,
                target_tokens: vec![video_token_id],
                worker_tokens: vec![7, 8, video_token_id, video_token_id],
                routing_tokens: vec![7, 8, video_pad, video_pad],
            },
        ];

        let (tokens, prompt_len, infos) = apply_tracked_mm_replacements(
            None,
            &replacements,
            &[image_token_id, video_token_id],
            16,
            Some(image_token_id),
            Some(video_token_id),
            mm_routing::KvEventMmIdentity::PadValueTokens,
        )
        .unwrap();

        assert_eq!(prompt_len, 18);
        assert_eq!(&tokens[..14], &[image_pad; 14]);
        assert_eq!(&tokens[14..18], &[7, 8, video_pad, video_pad]);
        assert!(tokens[18..].iter().all(|token| *token == 0));
        assert!(infos.iter().all(Option::is_none));
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn tracked_replacements_preserve_image_video_image_order() {
        use dynamo_kv_router::protocols::pad_value_for_mm_hash;

        let image_token_id = 99;
        let video_token_id = 100;
        let replacement = |mm_hash, target_token| TrackedMmRoutingReplacement {
            mm_hash,
            target_tokens: vec![target_token],
            worker_tokens: vec![target_token, target_token],
            routing_tokens: vec![
                pad_value_for_mm_hash(mm_hash),
                pad_value_for_mm_hash(mm_hash),
            ],
        };
        let replacements = [
            replacement(41, image_token_id),
            replacement(42, video_token_id),
            replacement(43, image_token_id),
        ];

        let (tokens, prompt_len, infos) = apply_tracked_mm_replacements(
            None,
            &replacements,
            &[1, image_token_id, 2, video_token_id, 3, image_token_id, 4],
            16,
            Some(image_token_id),
            Some(video_token_id),
            mm_routing::KvEventMmIdentity::MmMetadata,
        )
        .unwrap();

        assert_eq!(prompt_len, 10);
        assert_eq!(
            &tokens[..prompt_len],
            &[
                1,
                pad_value_for_mm_hash(41),
                pad_value_for_mm_hash(41),
                2,
                pad_value_for_mm_hash(42),
                pad_value_for_mm_hash(42),
                3,
                pad_value_for_mm_hash(43),
                pad_value_for_mm_hash(43),
                4,
            ]
        );
        assert!(infos[0].is_none());
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn tracked_replacements_reject_misordered_missing_and_extra_targets() {
        let replacement = |mm_hash, target| TrackedMmRoutingReplacement {
            mm_hash,
            target_tokens: vec![target],
            worker_tokens: vec![target],
            routing_tokens: vec![target],
        };
        let replacements = [replacement(41, 10), replacement(42, 20)];

        for token_ids in [&[20, 10][..], &[10][..], &[10, 20, 20][..]] {
            assert!(
                apply_tracked_mm_replacements(
                    None,
                    &replacements,
                    token_ids,
                    4,
                    Some(10),
                    Some(20),
                    mm_routing::KvEventMmIdentity::MmMetadata,
                )
                .is_err(),
                "invalid target sequence {token_ids:?} must fail closed"
            );
        }
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn shared_placeholder_video_matches_legacy_worker_normalization() {
        use dynamo_kv_router::protocols::pad_value_for_mm_hash;

        let mm_hash = 41;
        let pad = pad_value_for_mm_hash(mm_hash);
        let replacement = TrackedMmRoutingReplacement {
            mm_hash,
            target_tokens: vec![7],
            worker_tokens: vec![100, 19, 18, 18, 20, 101, 19, 18, 20],
            routing_tokens: vec![100, 19, pad, pad, 20, 101, 19, pad, 20],
        };

        let (tokens, prompt_len, block_infos) = apply_tracked_mm_replacements(
            None,
            &[replacement],
            &[7],
            4,
            Some(18),
            None,
            mm_routing::KvEventMmIdentity::MmMetadata,
        )
        .unwrap();

        assert_eq!(prompt_len, 9);
        assert_eq!(tokens, [100, 19, pad, pad, 20, 101, 19, pad, 20, 0, 0, 0]);
        assert_eq!(block_infos.len(), 3);
        assert!(block_infos[0].is_none());
        assert!(block_infos[1].is_none());
        assert_eq!(
            block_infos[2]
                .as_ref()
                .unwrap()
                .mm_objects
                .iter()
                .map(|object| object.mm_hash)
                .collect::<Vec<_>>(),
            [mm_hash]
        );
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn routing_bos_preserves_image_behavior_and_skips_video_only_requests() {
        assert_eq!(routing_bos_to_prepend(Some(1), true), Some(1));
        assert_eq!(routing_bos_to_prepend(None, true), None);
        assert_eq!(routing_bos_to_prepend(Some(1), false), None);
    }

    #[cfg(feature = "mm-routing")]
    #[test]
    fn consecutive_video_entries_are_not_exactly_routable() {
        let video = |mm_hash| MmRoutingEntry::Video {
            mm_hash,
            placeholder_token_id: 3,
            event_video_token_id: Some(3),
            target_tokens: vec![3],
            replacement_tokens: vec![3],
        };
        let image = MmRoutingEntry::Image {
            mm_hash: 2,
            width: 1,
            height: 1,
        };

        assert!(exact_mm_routing_entries_are_unambiguous(&[video(1)]));
        assert!(!exact_mm_routing_entries_are_unambiguous(&[
            video(1),
            video(2)
        ]));
        assert!(exact_mm_routing_entries_are_unambiguous(&[
            video(1),
            image,
            video(2)
        ]));
    }
}