# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import asyncio
import atexit
import gc
import importlib
import inspect
import json
import multiprocessing
import os
import signal
import socket
import tempfile
import uuid
from argparse import Namespace
from collections.abc import AsyncIterator, Awaitable
from contextlib import asynccontextmanager
from functools import partial
from http import HTTPStatus
from typing import Annotated, Any, Callable, Optional

import prometheus_client
import pydantic
import regex as re
import uvloop
from fastapi import APIRouter, Depends, FastAPI, Form, HTTPException, Request
from fastapi.exceptions import RequestValidationError
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse, Response, StreamingResponse
from prometheus_client import make_asgi_app
from prometheus_fastapi_instrumentator import Instrumentator
from starlette.concurrency import iterate_in_threadpool
from starlette.datastructures import URL, Headers, MutableHeaders, State
from starlette.routing import Mount
from starlette.types import ASGIApp, Message, Receive, Scope, Send
from typing_extensions import assert_never

import vllm.envs as envs
from vllm.config import VllmConfig
from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.engine.async_llm_engine import AsyncLLMEngine  # type: ignore
from vllm.engine.multiprocessing.client import MQLLMEngineClient
from vllm.engine.multiprocessing.engine import run_mp_engine
from vllm.engine.protocol import EngineClient
from vllm.entrypoints.chat_utils import (load_chat_template,
                                         resolve_hf_chat_template,
                                         resolve_mistral_chat_template)
from vllm.entrypoints.launcher import serve_http
from vllm.entrypoints.logger import RequestLogger
from vllm.entrypoints.openai.cli_args import (make_arg_parser,
                                              validate_parsed_serve_args)
# yapf conflicts with isort for this block
# yapf: disable
from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                              ChatCompletionResponse,
                                              ClassificationRequest,
                                              ClassificationResponse,
                                              CompletionRequest,
                                              CompletionResponse,
                                              DetokenizeRequest,
                                              DetokenizeResponse,
                                              EmbeddingRequest,
                                              EmbeddingResponse, ErrorResponse,
                                              LoadLoRAAdapterRequest,
                                              PoolingRequest, PoolingResponse,
                                              RerankRequest, RerankResponse,
                                              ResponsesRequest,
                                              ResponsesResponse, ScoreRequest,
                                              ScoreResponse, TokenizeRequest,
                                              TokenizeResponse,
                                              TranscriptionRequest,
                                              TranscriptionResponse,
                                              TranslationRequest,
                                              TranslationResponse,
                                              UnloadLoRAAdapterRequest)
# yapf: enable
from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
from vllm.entrypoints.openai.serving_classification import (
    ServingClassification)
from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
from vllm.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
from vllm.entrypoints.openai.serving_engine import OpenAIServing
from vllm.entrypoints.openai.serving_models import (BaseModelPath,
                                                    LoRAModulePath,
                                                    OpenAIServingModels)
from vllm.entrypoints.openai.serving_pooling import OpenAIServingPooling
from vllm.entrypoints.openai.serving_responses import OpenAIServingResponses
from vllm.entrypoints.openai.serving_score import ServingScores
from vllm.entrypoints.openai.serving_tokenization import (
    OpenAIServingTokenization)
from vllm.entrypoints.openai.serving_transcription import (
    OpenAIServingTranscription, OpenAIServingTranslation)
from vllm.entrypoints.openai.tool_parsers import ToolParserManager
from vllm.entrypoints.utils import (cli_env_setup, load_aware_call,
                                    log_non_default_args, with_cancellation)
from vllm.logger import init_logger
from vllm.reasoning import ReasoningParserManager
from vllm.transformers_utils.config import (
    maybe_register_config_serialize_by_value)
from vllm.transformers_utils.tokenizer import MistralTokenizer
from vllm.usage.usage_lib import UsageContext
from vllm.utils import (Device, FlexibleArgumentParser, decorate_logs,
                        get_open_zmq_ipc_path, is_valid_ipv6_address,
                        set_process_title, set_ulimit)
from vllm.v1.metrics.prometheus import get_prometheus_registry
from vllm.version import __version__ as VLLM_VERSION

prometheus_multiproc_dir: tempfile.TemporaryDirectory

# Cannot use __name__ (https://github.com/vllm-project/vllm/pull/4765)
logger = init_logger('vllm.entrypoints.openai.api_server')

_running_tasks: set[asyncio.Task] = set()


@asynccontextmanager
async def lifespan(app: FastAPI):
    try:
        if app.state.log_stats:
            engine_client: EngineClient = app.state.engine_client

            async def _force_log():
                while True:
                    await asyncio.sleep(10.)
                    await engine_client.do_log_stats()

            task = asyncio.create_task(_force_log())
            _running_tasks.add(task)
            task.add_done_callback(_running_tasks.remove)
        else:
            task = None

        # Mark the startup heap as static so that it's ignored by GC.
        # Reduces pause times of oldest generation collections.
        gc.collect()
        gc.freeze()
        try:
            yield
        finally:
            if task is not None:
                task.cancel()
    finally:
        # Ensure app state including engine ref is gc'd
        del app.state


@asynccontextmanager
async def build_async_engine_client(
    args: Namespace,
    *,
    usage_context: UsageContext = UsageContext.OPENAI_API_SERVER,
    disable_frontend_multiprocessing: Optional[bool] = None,
    client_config: Optional[dict[str, Any]] = None,
) -> AsyncIterator[EngineClient]:

    # Context manager to handle engine_client lifecycle
    # Ensures everything is shutdown and cleaned up on error/exit
    engine_args = AsyncEngineArgs.from_cli_args(args)

    if disable_frontend_multiprocessing is None:
        disable_frontend_multiprocessing = bool(
            args.disable_frontend_multiprocessing)

    async with build_async_engine_client_from_engine_args(
            engine_args,
            usage_context=usage_context,
            disable_frontend_multiprocessing=disable_frontend_multiprocessing,
            client_config=client_config,
    ) as engine:
        yield engine


@asynccontextmanager
async def build_async_engine_client_from_engine_args(
    engine_args: AsyncEngineArgs,
    *,
    usage_context: UsageContext = UsageContext.OPENAI_API_SERVER,
    disable_frontend_multiprocessing: bool = False,
    client_config: Optional[dict[str, Any]] = None,
) -> AsyncIterator[EngineClient]:
    """
    Create EngineClient, either:
        - in-process using the AsyncLLMEngine Directly
        - multiprocess using AsyncLLMEngine RPC

    Returns the Client or None if the creation failed.
    """

    # Create the EngineConfig (determines if we can use V1).
    vllm_config = engine_args.create_engine_config(usage_context=usage_context)

    # V1 AsyncLLM.
    if envs.VLLM_USE_V1:
        if disable_frontend_multiprocessing:
            logger.warning(
                "V1 is enabled, but got --disable-frontend-multiprocessing. "
                "To disable frontend multiprocessing, set VLLM_USE_V1=0.")

        from vllm.v1.engine.async_llm import AsyncLLM
        async_llm: Optional[AsyncLLM] = None
        client_index = client_config.pop(
            "client_index") if client_config else 0
        try:
            async_llm = AsyncLLM.from_vllm_config(
                vllm_config=vllm_config,
                usage_context=usage_context,
                disable_log_requests=engine_args.disable_log_requests,
                disable_log_stats=engine_args.disable_log_stats,
                client_addresses=client_config,
                client_index=client_index)

            # Don't keep the dummy data in memory
            await async_llm.reset_mm_cache()

            yield async_llm
        finally:
            if async_llm:
                async_llm.shutdown()

    # V0 AsyncLLM.
    elif (MQLLMEngineClient.is_unsupported_config(vllm_config)
          or disable_frontend_multiprocessing):

        engine_client: Optional[EngineClient] = None
        try:
            engine_client = AsyncLLMEngine.from_vllm_config(
                vllm_config=vllm_config,
                usage_context=usage_context,
                disable_log_requests=engine_args.disable_log_requests,
                disable_log_stats=engine_args.disable_log_stats)
            yield engine_client
        finally:
            if engine_client and hasattr(engine_client, "shutdown"):
                engine_client.shutdown()

    # V0MQLLMEngine.
    else:
        if "PROMETHEUS_MULTIPROC_DIR" not in os.environ:
            # Make TemporaryDirectory for prometheus multiprocessing
            # Note: global TemporaryDirectory will be automatically
            #   cleaned up upon exit.
            global prometheus_multiproc_dir
            prometheus_multiproc_dir = tempfile.TemporaryDirectory()
            os.environ[
                "PROMETHEUS_MULTIPROC_DIR"] = prometheus_multiproc_dir.name
        else:
            logger.warning(
                "Found PROMETHEUS_MULTIPROC_DIR was set by user. "
                "This directory must be wiped between vLLM runs or "
                "you will find inaccurate metrics. Unset the variable "
                "and vLLM will properly handle cleanup.")

        # Select random path for IPC.
        ipc_path = get_open_zmq_ipc_path()
        logger.debug("Multiprocessing frontend to use %s for IPC Path.",
                     ipc_path)

        # Start RPCServer in separate process (holds the LLMEngine).
        # the current process might have CUDA context,
        # so we need to spawn a new process
        context = multiprocessing.get_context("spawn")

        # Ensure we can serialize transformer config before spawning
        maybe_register_config_serialize_by_value()

        # The Process can raise an exception during startup, which may
        # not actually result in an exitcode being reported. As a result
        # we use a shared variable to communicate the information.
        engine_alive = multiprocessing.Value('b', True, lock=False)
        engine_process = context.Process(
            target=run_mp_engine,
            args=(vllm_config, UsageContext.OPENAI_API_SERVER, ipc_path,
                  engine_args.disable_log_stats,
                  engine_args.disable_log_requests, engine_alive))
        engine_process.start()
        engine_pid = engine_process.pid
        assert engine_pid is not None, "Engine process failed to start."
        logger.info("Started engine process with PID %d", engine_pid)

        def _cleanup_ipc_path():
            socket_path = ipc_path.replace("ipc://", "")
            if os.path.exists(socket_path):
                os.remove(socket_path)

        # Ensure we clean up the local IPC socket file on exit.
        atexit.register(_cleanup_ipc_path)

        # Build RPCClient, which conforms to EngineClient Protocol.
        build_client = partial(MQLLMEngineClient, ipc_path, vllm_config,
                               engine_pid)
        mq_engine_client = await asyncio.get_running_loop().run_in_executor(
            None, build_client)
        try:
            while True:
                try:
                    await mq_engine_client.setup()
                    break
                except TimeoutError:
                    if (not engine_process.is_alive()
                            or not engine_alive.value):
                        raise RuntimeError(
                            "Engine process failed to start. See stack "
                            "trace for the root cause.") from None

            yield mq_engine_client  # type: ignore[misc]
        finally:
            # Ensure rpc server process was terminated
            engine_process.terminate()

            # Close all open connections to the backend
            mq_engine_client.close()

            # Wait for engine process to join
            engine_process.join(4)
            if engine_process.exitcode is None:
                # Kill if taking longer than 5 seconds to stop
                engine_process.kill()

            # Lazy import for prometheus multiprocessing.
            # We need to set PROMETHEUS_MULTIPROC_DIR environment variable
            # before prometheus_client is imported.
            # See https://prometheus.github.io/client_python/multiprocess/
            from prometheus_client import multiprocess
            multiprocess.mark_process_dead(engine_process.pid)


async def validate_json_request(raw_request: Request):
    content_type = raw_request.headers.get("content-type", "").lower()
    media_type = content_type.split(";", maxsplit=1)[0]
    if media_type != "application/json":
        raise RequestValidationError(errors=[
            "Unsupported Media Type: Only 'application/json' is allowed"
        ])


router = APIRouter()


class PrometheusResponse(Response):
    media_type = prometheus_client.CONTENT_TYPE_LATEST


def mount_metrics(app: FastAPI):
    """Mount prometheus metrics to a FastAPI app."""

    registry = get_prometheus_registry()

    # `response_class=PrometheusResponse` is needed to return an HTTP response
    # with header "Content-Type: text/plain; version=0.0.4; charset=utf-8"
    # instead of the default "application/json" which is incorrect.
    # See https://github.com/trallnag/prometheus-fastapi-instrumentator/issues/163#issue-1296092364
    Instrumentator(
        excluded_handlers=[
            "/metrics",
            "/health",
            "/load",
            "/ping",
            "/version",
            "/server_info",
        ],
        registry=registry,
    ).add().instrument(app).expose(app, response_class=PrometheusResponse)

    # Add prometheus asgi middleware to route /metrics requests
    metrics_route = Mount("/metrics", make_asgi_app(registry=registry))

    # Workaround for 307 Redirect for /metrics
    metrics_route.path_regex = re.compile("^/metrics(?P<path>.*)$")
    app.routes.append(metrics_route)


def base(request: Request) -> OpenAIServing:
    # Reuse the existing instance
    return tokenization(request)


def models(request: Request) -> OpenAIServingModels:
    return request.app.state.openai_serving_models


def responses(request: Request) -> Optional[OpenAIServingResponses]:
    return request.app.state.openai_serving_responses


def chat(request: Request) -> Optional[OpenAIServingChat]:
    return request.app.state.openai_serving_chat


def completion(request: Request) -> Optional[OpenAIServingCompletion]:
    return request.app.state.openai_serving_completion


def pooling(request: Request) -> Optional[OpenAIServingPooling]:
    return request.app.state.openai_serving_pooling


def embedding(request: Request) -> Optional[OpenAIServingEmbedding]:
    return request.app.state.openai_serving_embedding


def score(request: Request) -> Optional[ServingScores]:
    return request.app.state.openai_serving_scores


def classify(request: Request) -> Optional[ServingClassification]:
    return request.app.state.openai_serving_classification


def rerank(request: Request) -> Optional[ServingScores]:
    return request.app.state.openai_serving_scores


def tokenization(request: Request) -> OpenAIServingTokenization:
    return request.app.state.openai_serving_tokenization


def transcription(request: Request) -> OpenAIServingTranscription:
    return request.app.state.openai_serving_transcription


def translation(request: Request) -> OpenAIServingTranslation:
    return request.app.state.openai_serving_translation


def engine_client(request: Request) -> EngineClient:
    return request.app.state.engine_client


@router.get("/health", response_class=Response)
async def health(raw_request: Request) -> Response:
    """Health check."""
    await engine_client(raw_request).check_health()
    return Response(status_code=200)


@router.get("/load")
async def get_server_load_metrics(request: Request):
    # This endpoint returns the current server load metrics.
    # It tracks requests utilizing the GPU from the following routes:
    # - /v1/chat/completions
    # - /v1/completions
    # - /v1/audio/transcriptions
    # - /v1/audio/translations
    # - /v1/embeddings
    # - /pooling
    # - /classify
    # - /score
    # - /v1/score
    # - /rerank
    # - /v1/rerank
    # - /v2/rerank
    return JSONResponse(
        content={'server_load': request.app.state.server_load_metrics})


@router.get("/ping", response_class=Response)
@router.post("/ping", response_class=Response)
async def ping(raw_request: Request) -> Response:
    """Ping check. Endpoint required for SageMaker"""
    return await health(raw_request)


@router.post("/tokenize",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.NOT_FOUND.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.NOT_IMPLEMENTED.value: {
                     "model": ErrorResponse
                 },
             })
@with_cancellation
async def tokenize(request: TokenizeRequest, raw_request: Request):
    handler = tokenization(raw_request)

    try:
        generator = await handler.create_tokenize(request, raw_request)
    except NotImplementedError as e:
        raise HTTPException(status_code=HTTPStatus.NOT_IMPLEMENTED.value,
                            detail=str(e)) from e
    except Exception as e:
        raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
                            detail=str(e)) from e

    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
    elif isinstance(generator, TokenizeResponse):
        return JSONResponse(content=generator.model_dump())

    assert_never(generator)


@router.post("/detokenize",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.NOT_FOUND.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
@with_cancellation
async def detokenize(request: DetokenizeRequest, raw_request: Request):
    handler = tokenization(raw_request)

    try:
        generator = await handler.create_detokenize(request, raw_request)
    except OverflowError as e:
        raise RequestValidationError(errors=[str(e)]) from e
    except Exception as e:
        raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
                            detail=str(e)) from e

    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
    elif isinstance(generator, DetokenizeResponse):
        return JSONResponse(content=generator.model_dump())

    assert_never(generator)


def maybe_register_tokenizer_info_endpoint(args):
    """Conditionally register the tokenizer info endpoint if enabled."""
    if getattr(args, 'enable_tokenizer_info_endpoint', False):

        @router.get("/tokenizer_info")
        async def get_tokenizer_info(raw_request: Request):
            """Get comprehensive tokenizer information."""
            result = await tokenization(raw_request).get_tokenizer_info()
            return JSONResponse(content=result.model_dump(),
                                status_code=result.code if isinstance(
                                    result, ErrorResponse) else 200)


@router.get("/v1/models")
async def show_available_models(raw_request: Request):
    handler = models(raw_request)

    models_ = await handler.show_available_models()
    return JSONResponse(content=models_.model_dump())


@router.get("/version")
async def show_version():
    ver = {"version": VLLM_VERSION}
    return JSONResponse(content=ver)


@router.post("/v1/responses",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.OK.value: {
                     "content": {
                         "text/event-stream": {}
                     }
                 },
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.NOT_FOUND.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
@with_cancellation
async def create_responses(request: ResponsesRequest, raw_request: Request):
    handler = responses(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Responses API")

    generator = await handler.create_responses(request, raw_request)

    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
    elif isinstance(generator, ResponsesResponse):
        return JSONResponse(content=generator.model_dump())
    return StreamingResponse(content=generator, media_type="text/event-stream")


@router.get("/v1/responses/{response_id}")
async def retrieve_responses(response_id: str, raw_request: Request):
    handler = responses(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Responses API")

    response = await handler.retrieve_responses(response_id)

    if isinstance(response, ErrorResponse):
        return JSONResponse(content=response.model_dump(),
                            status_code=response.code)
    return JSONResponse(content=response.model_dump())


@router.post("/v1/responses/{response_id}/cancel")
async def cancel_responses(response_id: str, raw_request: Request):
    handler = responses(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Responses API")

    response = await handler.cancel_responses(response_id)

    if isinstance(response, ErrorResponse):
        return JSONResponse(content=response.model_dump(),
                            status_code=response.code)
    return JSONResponse(content=response.model_dump())


@router.post("/v1/chat/completions",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.OK.value: {
                     "content": {
                         "text/event-stream": {}
                     }
                 },
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.NOT_FOUND.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 }
             })
@with_cancellation
@load_aware_call
async def create_chat_completion(request: ChatCompletionRequest,
                                 raw_request: Request):
    handler = chat(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Chat Completions API")

    generator = await handler.create_chat_completion(request, raw_request)

    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)

    elif isinstance(generator, ChatCompletionResponse):
        return JSONResponse(content=generator.model_dump())

    return StreamingResponse(content=generator, media_type="text/event-stream")


@router.post("/v1/completions",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.OK.value: {
                     "content": {
                         "text/event-stream": {}
                     }
                 },
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.NOT_FOUND.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
@with_cancellation
@load_aware_call
async def create_completion(request: CompletionRequest, raw_request: Request):
    handler = completion(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Completions API")

    try:
        generator = await handler.create_completion(request, raw_request)
    except OverflowError as e:
        raise HTTPException(status_code=HTTPStatus.BAD_REQUEST.value,
                            detail=str(e)) from e
    except Exception as e:
        raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
                            detail=str(e)) from e

    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
    elif isinstance(generator, CompletionResponse):
        return JSONResponse(content=generator.model_dump())

    return StreamingResponse(content=generator, media_type="text/event-stream")


@router.post("/v1/embeddings",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
@with_cancellation
@load_aware_call
async def create_embedding(request: EmbeddingRequest, raw_request: Request):
    handler = embedding(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Embeddings API")

    generator = await handler.create_embedding(request, raw_request)

    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
    elif isinstance(generator, EmbeddingResponse):
        return JSONResponse(content=generator.model_dump())

    assert_never(generator)


@router.post("/pooling",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
@with_cancellation
@load_aware_call
async def create_pooling(request: PoolingRequest, raw_request: Request):
    handler = pooling(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Pooling API")

    generator = await handler.create_pooling(request, raw_request)
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
    elif isinstance(generator, PoolingResponse):
        return JSONResponse(content=generator.model_dump())

    assert_never(generator)


@router.post("/classify", dependencies=[Depends(validate_json_request)])
@with_cancellation
@load_aware_call
async def create_classify(request: ClassificationRequest,
                          raw_request: Request):
    handler = classify(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Classification API")

    generator = await handler.create_classify(request, raw_request)
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)

    elif isinstance(generator, ClassificationResponse):
        return JSONResponse(content=generator.model_dump())

    assert_never(generator)


@router.post("/score",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
@with_cancellation
@load_aware_call
async def create_score(request: ScoreRequest, raw_request: Request):
    handler = score(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Score API")

    generator = await handler.create_score(request, raw_request)
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
    elif isinstance(generator, ScoreResponse):
        return JSONResponse(content=generator.model_dump())

    assert_never(generator)


@router.post("/v1/score",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
@with_cancellation
@load_aware_call
async def create_score_v1(request: ScoreRequest, raw_request: Request):
    logger.warning(
        "To indicate that Score API is not part of standard OpenAI API, we "
        "have moved it to `/score`. Please update your client accordingly.")

    return await create_score(request, raw_request)


@router.post("/v1/audio/transcriptions",
             responses={
                 HTTPStatus.OK.value: {
                     "content": {
                         "text/event-stream": {}
                     }
                 },
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.UNPROCESSABLE_ENTITY.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
@with_cancellation
@load_aware_call
async def create_transcriptions(raw_request: Request,
                                request: Annotated[TranscriptionRequest,
                                                   Form()]):
    handler = transcription(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Transcriptions API")

    audio_data = await request.file.read()
    generator = await handler.create_transcription(audio_data, request,
                                                   raw_request)

    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)

    elif isinstance(generator, TranscriptionResponse):
        return JSONResponse(content=generator.model_dump())

    return StreamingResponse(content=generator, media_type="text/event-stream")


@router.post("/v1/audio/translations",
             responses={
                 HTTPStatus.OK.value: {
                     "content": {
                         "text/event-stream": {}
                     }
                 },
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.UNPROCESSABLE_ENTITY.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
@with_cancellation
@load_aware_call
async def create_translations(request: Annotated[TranslationRequest,
                                                 Form()],
                              raw_request: Request):
    handler = translation(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Translations API")

    audio_data = await request.file.read()
    generator = await handler.create_translation(audio_data, request,
                                                 raw_request)

    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)

    elif isinstance(generator, TranslationResponse):
        return JSONResponse(content=generator.model_dump())

    return StreamingResponse(content=generator, media_type="text/event-stream")


@router.post("/rerank",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
@with_cancellation
@load_aware_call
async def do_rerank(request: RerankRequest, raw_request: Request):
    handler = rerank(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Rerank (Score) API")
    generator = await handler.do_rerank(request, raw_request)
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
    elif isinstance(generator, RerankResponse):
        return JSONResponse(content=generator.model_dump())

    assert_never(generator)


@router.post("/v1/rerank",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
@with_cancellation
async def do_rerank_v1(request: RerankRequest, raw_request: Request):
    logger.warning_once(
        "To indicate that the rerank API is not part of the standard OpenAI"
        " API, we have located it at `/rerank`. Please update your client "
        "accordingly. (Note: Conforms to JinaAI rerank API)")

    return await do_rerank(request, raw_request)


@router.post("/v2/rerank",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
@with_cancellation
async def do_rerank_v2(request: RerankRequest, raw_request: Request):
    return await do_rerank(request, raw_request)


if envs.VLLM_SERVER_DEV_MODE:
    logger.warning("SECURITY WARNING: Development endpoints are enabled! "
                   "This should NOT be used in production!")

    @router.get("/server_info")
    async def show_server_info(raw_request: Request):
        server_info = {"vllm_config": str(raw_request.app.state.vllm_config)}
        return JSONResponse(content=server_info)

    @router.post("/reset_prefix_cache")
    async def reset_prefix_cache(raw_request: Request):
        """
        Reset the prefix cache. Note that we currently do not check if the
        prefix cache is successfully reset in the API server.
        """
        device = None
        device_str = raw_request.query_params.get("device")
        if device_str is not None:
            device = Device[device_str.upper()]
        logger.info("Resetting prefix cache with specific %s...", str(device))
        await engine_client(raw_request).reset_prefix_cache(device)
        return Response(status_code=200)

    @router.post("/sleep")
    async def sleep(raw_request: Request):
        # get POST params
        level = raw_request.query_params.get("level", "1")
        await engine_client(raw_request).sleep(int(level))
        # FIXME: in v0 with frontend multiprocessing, the sleep command
        # is sent but does not finish yet when we return a response.
        return Response(status_code=200)

    @router.post("/wake_up")
    async def wake_up(raw_request: Request):
        tags = raw_request.query_params.getlist("tags")
        if tags == []:
            # set to None to wake up all tags if no tags are provided
            tags = None
        logger.info("wake up the engine with tags: %s", tags)
        await engine_client(raw_request).wake_up(tags)
        # FIXME: in v0 with frontend multiprocessing, the wake-up command
        # is sent but does not finish yet when we return a response.
        return Response(status_code=200)

    @router.get("/is_sleeping")
    async def is_sleeping(raw_request: Request):
        logger.info("check whether the engine is sleeping")
        is_sleeping = await engine_client(raw_request).is_sleeping()
        return JSONResponse(content={"is_sleeping": is_sleeping})


@router.post("/scale_elastic_ep",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.OK.value: {
                     "model": dict
                 },
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.REQUEST_TIMEOUT.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
async def scale_elastic_ep(raw_request: Request):
    try:
        body = await raw_request.json()
    except json.JSONDecodeError as e:
        raise HTTPException(status_code=400,
                            detail="Invalid JSON format") from e  # noqa: B904

    new_data_parallel_size = body.get("new_data_parallel_size")
    drain_timeout = body.get("drain_timeout", 120)  # Default 2 minutes

    if new_data_parallel_size is None:
        raise HTTPException(status_code=400,
                            detail="new_data_parallel_size is required")

    if not isinstance(new_data_parallel_size,
                      int) or new_data_parallel_size <= 0:
        raise HTTPException(
            status_code=400,
            detail="new_data_parallel_size must be a positive integer")

    if not isinstance(drain_timeout, int) or drain_timeout <= 0:
        raise HTTPException(status_code=400,
                            detail="drain_timeout must be a positive integer")

    # Set scaling flag to prevent new requests
    global _scaling_elastic_ep
    _scaling_elastic_ep = True
    client = engine_client(raw_request)
    try:
        await client.scale_elastic_ep(new_data_parallel_size, drain_timeout)
        return JSONResponse({
            "message":
            f"Scaled to {new_data_parallel_size} "
            "data parallel engines",
        })
    except TimeoutError as e:
        raise HTTPException(status_code=408,
                            detail="Scale failed due to request drain timeout "
                            f"after {drain_timeout} seconds") from e
    except Exception as e:
        logger.error("Scale failed: %s", e)
        raise HTTPException(status_code=500, detail="Scale failed") from e
    finally:
        _scaling_elastic_ep = False


@router.post("/is_scaling_elastic_ep")
async def is_scaling_elastic_ep(raw_request: Request):
    return JSONResponse({"is_scaling_elastic_ep": _scaling_elastic_ep})


# TODO: RequestType = TypeForm[BaseModel] when recognized by type checkers
# (requires typing_extensions >= 4.13)
RequestType = Any
GetHandlerFn = Callable[[Request], Optional[OpenAIServing]]
EndpointFn = Callable[[RequestType, Request], Awaitable[Any]]

# NOTE: Items defined earlier take higher priority
INVOCATION_TYPES: list[tuple[RequestType, tuple[GetHandlerFn, EndpointFn]]] = [
    (ChatCompletionRequest, (chat, create_chat_completion)),
    (CompletionRequest, (completion, create_completion)),
    (EmbeddingRequest, (embedding, create_embedding)),
    (ClassificationRequest, (classify, create_classify)),
    (ScoreRequest, (score, create_score)),
    (RerankRequest, (rerank, do_rerank)),
    (PoolingRequest, (pooling, create_pooling)),
]

# NOTE: Construct the TypeAdapters only once
INVOCATION_VALIDATORS = [
    (pydantic.TypeAdapter(request_type), (get_handler, endpoint))
    for request_type, (get_handler, endpoint) in INVOCATION_TYPES
]


@router.post("/invocations",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.UNSUPPORTED_MEDIA_TYPE.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
async def invocations(raw_request: Request):
    """For SageMaker, routes requests based on the request type."""
    try:
        body = await raw_request.json()
    except json.JSONDecodeError as e:
        raise HTTPException(status_code=HTTPStatus.BAD_REQUEST.value,
                            detail=f"JSON decode error: {e}") from e

    valid_endpoints = [(validator, endpoint)
                       for validator, (get_handler,
                                       endpoint) in INVOCATION_VALIDATORS
                       if get_handler(raw_request) is not None]

    for request_validator, endpoint in valid_endpoints:
        try:
            request = request_validator.validate_python(body)
        except pydantic.ValidationError:
            continue

        return await endpoint(request, raw_request)

    type_names = [
        t.__name__ if isinstance(t := validator._type, type) else str(t)
        for validator, _ in valid_endpoints
    ]
    msg = ("Cannot find suitable handler for request. "
           f"Expected one of: {type_names}")
    res = base(raw_request).create_error_response(message=msg)
    return JSONResponse(content=res.model_dump(), status_code=res.code)


if envs.VLLM_TORCH_PROFILER_DIR:
    logger.warning(
        "Torch Profiler is enabled in the API server. This should ONLY be "
        "used for local development!")

    @router.post("/start_profile")
    async def start_profile(raw_request: Request):
        logger.info("Starting profiler...")
        await engine_client(raw_request).start_profile()
        logger.info("Profiler started.")
        return Response(status_code=200)

    @router.post("/stop_profile")
    async def stop_profile(raw_request: Request):
        logger.info("Stopping profiler...")
        await engine_client(raw_request).stop_profile()
        logger.info("Profiler stopped.")
        return Response(status_code=200)


if envs.VLLM_ALLOW_RUNTIME_LORA_UPDATING:
    logger.warning(
        "LoRA dynamic loading & unloading is enabled in the API server. "
        "This should ONLY be used for local development!")

    @router.post("/v1/load_lora_adapter",
                 dependencies=[Depends(validate_json_request)])
    async def load_lora_adapter(request: LoadLoRAAdapterRequest,
                                raw_request: Request):
        handler = models(raw_request)
        response = await handler.load_lora_adapter(request)
        if isinstance(response, ErrorResponse):
            return JSONResponse(content=response.model_dump(),
                                status_code=response.code)

        return Response(status_code=200, content=response)

    @router.post("/v1/unload_lora_adapter",
                 dependencies=[Depends(validate_json_request)])
    async def unload_lora_adapter(request: UnloadLoRAAdapterRequest,
                                  raw_request: Request):
        handler = models(raw_request)
        response = await handler.unload_lora_adapter(request)
        if isinstance(response, ErrorResponse):
            return JSONResponse(content=response.model_dump(),
                                status_code=response.code)

        return Response(status_code=200, content=response)


def load_log_config(log_config_file: Optional[str]) -> Optional[dict]:
    if not log_config_file:
        return None
    try:
        with open(log_config_file) as f:
            return json.load(f)
    except Exception as e:
        logger.warning("Failed to load log config from file %s: error %s",
                       log_config_file, e)
        return None


class AuthenticationMiddleware:
    """
    Pure ASGI middleware that authenticates each request by checking
    if the Authorization header exists and equals "Bearer {api_key}".

    Notes
    -----
    There are two cases in which authentication is skipped:
        1. The HTTP method is OPTIONS.
        2. The request path doesn't start with /v1 (e.g. /health).
    """

    def __init__(self, app: ASGIApp, tokens: list[str]) -> None:
        self.app = app
        self.api_tokens = {f"Bearer {token}" for token in tokens}

    def __call__(self, scope: Scope, receive: Receive,
                 send: Send) -> Awaitable[None]:
        if scope["type"] not in ("http",
                                 "websocket") or scope["method"] == "OPTIONS":
            # scope["type"] can be "lifespan" or "startup" for example,
            # in which case we don't need to do anything
            return self.app(scope, receive, send)
        root_path = scope.get("root_path", "")
        url_path = URL(scope=scope).path.removeprefix(root_path)
        headers = Headers(scope=scope)
        # Type narrow to satisfy mypy.
        if url_path.startswith("/v1") and headers.get(
                "Authorization") not in self.api_tokens:
            response = JSONResponse(content={"error": "Unauthorized"},
                                    status_code=401)
            return response(scope, receive, send)
        return self.app(scope, receive, send)


class XRequestIdMiddleware:
    """
    Middleware the set's the X-Request-Id header for each response
    to a random uuid4 (hex) value if the header isn't already
    present in the request, otherwise use the provided request id.
    """

    def __init__(self, app: ASGIApp) -> None:
        self.app = app

    def __call__(self, scope: Scope, receive: Receive,
                 send: Send) -> Awaitable[None]:
        if scope["type"] not in ("http", "websocket"):
            return self.app(scope, receive, send)

        # Extract the request headers.
        request_headers = Headers(scope=scope)

        async def send_with_request_id(message: Message) -> None:
            """
            Custom send function to mutate the response headers
            and append X-Request-Id to it.
            """
            if message["type"] == "http.response.start":
                response_headers = MutableHeaders(raw=message["headers"])
                request_id = request_headers.get("X-Request-Id",
                                                 uuid.uuid4().hex)
                response_headers.append("X-Request-Id", request_id)
            await send(message)

        return self.app(scope, receive, send_with_request_id)


# Global variable to track scaling state
_scaling_elastic_ep = False


class ScalingMiddleware:
    """
    Middleware that checks if the model is currently scaling and
    returns a 503 Service Unavailable response if it is.

    This middleware applies to all HTTP requests and prevents
    processing when the model is in a scaling state.
    """

    def __init__(self, app: ASGIApp) -> None:
        self.app = app

    def __call__(self, scope: Scope, receive: Receive,
                 send: Send) -> Awaitable[None]:
        if scope["type"] != "http":
            return self.app(scope, receive, send)

        # Check global scaling state
        global _scaling_elastic_ep
        if _scaling_elastic_ep:
            # Return 503 Service Unavailable response
            response = JSONResponse(content={
                "error":
                "The model is currently scaling. Please try again later."
            },
                                    status_code=503)
            return response(scope, receive, send)

        return self.app(scope, receive, send)


def _extract_content_from_chunk(chunk_data: dict) -> str:
    """Extract content from a streaming response chunk."""
    try:
        from vllm.entrypoints.openai.protocol import (
            ChatCompletionStreamResponse, CompletionStreamResponse)

        # Try using Completion types for type-safe parsing
        if chunk_data.get('object') == 'chat.completion.chunk':
            chat_response = ChatCompletionStreamResponse.model_validate(
                chunk_data)
            if chat_response.choices and chat_response.choices[0].delta.content:
                return chat_response.choices[0].delta.content
        elif chunk_data.get('object') == 'text_completion':
            completion_response = CompletionStreamResponse.model_validate(
                chunk_data)
            if completion_response.choices and completion_response.choices[
                    0].text:
                return completion_response.choices[0].text
    except pydantic.ValidationError:
        # Fallback to manual parsing
        if 'choices' in chunk_data and chunk_data['choices']:
            choice = chunk_data['choices'][0]
            if 'delta' in choice and choice['delta'].get('content'):
                return choice['delta']['content']
            elif choice.get('text'):
                return choice['text']
    return ""


class SSEDecoder:
    """Robust Server-Sent Events decoder for streaming responses."""

    def __init__(self):
        self.buffer = ""
        self.content_buffer = []

    def decode_chunk(self, chunk: bytes) -> list[dict]:
        """Decode a chunk of SSE data and return parsed events."""
        import json

        try:
            chunk_str = chunk.decode('utf-8')
        except UnicodeDecodeError:
            # Skip malformed chunks
            return []

        self.buffer += chunk_str
        events = []

        # Process complete lines
        while '\n' in self.buffer:
            line, self.buffer = self.buffer.split('\n', 1)
            line = line.rstrip('\r')  # Handle CRLF

            if line.startswith('data: '):
                data_str = line[6:].strip()
                if data_str == '[DONE]':
                    events.append({'type': 'done'})
                elif data_str:
                    try:
                        event_data = json.loads(data_str)
                        events.append({'type': 'data', 'data': event_data})
                    except json.JSONDecodeError:
                        # Skip malformed JSON
                        continue

        return events

    def extract_content(self, event_data: dict) -> str:
        """Extract content from event data."""
        return _extract_content_from_chunk(event_data)

    def add_content(self, content: str) -> None:
        """Add content to the buffer."""
        if content:
            self.content_buffer.append(content)

    def get_complete_content(self) -> str:
        """Get the complete buffered content."""
        return ''.join(self.content_buffer)


def _log_streaming_response(response, response_body: list) -> None:
    """Log streaming response with robust SSE parsing."""
    from starlette.concurrency import iterate_in_threadpool

    sse_decoder = SSEDecoder()
    chunk_count = 0

    def buffered_iterator():
        nonlocal chunk_count

        for chunk in response_body:
            chunk_count += 1
            yield chunk

            # Parse SSE events from chunk
            events = sse_decoder.decode_chunk(chunk)

            for event in events:
                if event['type'] == 'data':
                    content = sse_decoder.extract_content(event['data'])
                    sse_decoder.add_content(content)
                elif event['type'] == 'done':
                    # Log complete content when done
                    full_content = sse_decoder.get_complete_content()
                    if full_content:
                        # Truncate if too long
                        if len(full_content) > 2048:
                            full_content = full_content[:2048] + ""
                            "...[truncated]"
                        logger.info(
                            "response_body={streaming_complete: " \
                            "content='%s', chunks=%d}",
                            full_content, chunk_count)
                    else:
                        logger.info(
                            "response_body={streaming_complete: " \
                            "no_content, chunks=%d}",
                            chunk_count)
                    return

    response.body_iterator = iterate_in_threadpool(buffered_iterator())
    logger.info("response_body={streaming_started: chunks=%d}",
                len(response_body))


def _log_non_streaming_response(response_body: list) -> None:
    """Log non-streaming response."""
    try:
        decoded_body = response_body[0].decode()
        logger.info("response_body={%s}", decoded_body)
    except UnicodeDecodeError:
        logger.info("response_body={<binary_data>}")


def build_app(args: Namespace) -> FastAPI:
    if args.disable_fastapi_docs:
        app = FastAPI(openapi_url=None,
                      docs_url=None,
                      redoc_url=None,
                      lifespan=lifespan)
    else:
        app = FastAPI(lifespan=lifespan)
    app.include_router(router)
    app.root_path = args.root_path

    mount_metrics(app)

    app.add_middleware(
        CORSMiddleware,
        allow_origins=args.allowed_origins,
        allow_credentials=args.allow_credentials,
        allow_methods=args.allowed_methods,
        allow_headers=args.allowed_headers,
    )

    @app.exception_handler(HTTPException)
    async def http_exception_handler(_: Request, exc: HTTPException):
        err = ErrorResponse(message=exc.detail,
                            type=HTTPStatus(exc.status_code).phrase,
                            code=exc.status_code)
        return JSONResponse(err.model_dump(), status_code=exc.status_code)

    @app.exception_handler(RequestValidationError)
    async def validation_exception_handler(_: Request,
                                           exc: RequestValidationError):
        exc_str = str(exc)
        errors_str = str(exc.errors())

        if exc.errors() and errors_str and errors_str != exc_str:
            message = f"{exc_str} {errors_str}"
        else:
            message = exc_str

        err = ErrorResponse(message=message,
                            type=HTTPStatus.BAD_REQUEST.phrase,
                            code=HTTPStatus.BAD_REQUEST)
        return JSONResponse(err.model_dump(),
                            status_code=HTTPStatus.BAD_REQUEST)

    # Ensure --api-key option from CLI takes precedence over VLLM_API_KEY
    if tokens := [key for key in (args.api_key or [envs.VLLM_API_KEY]) if key]:
        app.add_middleware(AuthenticationMiddleware, tokens=tokens)

    if args.enable_request_id_headers:
        app.add_middleware(XRequestIdMiddleware)

    # Add scaling middleware to check for scaling state
    app.add_middleware(ScalingMiddleware)

    if envs.VLLM_DEBUG_LOG_API_SERVER_RESPONSE:
        logger.warning("CAUTION: Enabling log response in the API Server. "
                       "This can include sensitive information and should be "
                       "avoided in production.")

        @app.middleware("http")
        async def log_response(request: Request, call_next):
            response = await call_next(request)
            response_body = [
                section async for section in response.body_iterator
            ]
            response.body_iterator = iterate_in_threadpool(iter(response_body))
            # Check if this is a streaming response by looking at content-type
            content_type = response.headers.get("content-type", "")
            is_streaming = content_type == "text/event-stream; charset=utf-8"

            # Log response body based on type
            if not response_body:
                logger.info("response_body={<empty>}")
            elif is_streaming:
                _log_streaming_response(response, response_body)
            else:
                _log_non_streaming_response(response_body)
            return response

    for middleware in args.middleware:
        module_path, object_name = middleware.rsplit(".", 1)
        imported = getattr(importlib.import_module(module_path), object_name)
        if inspect.isclass(imported):
            app.add_middleware(imported)  # type: ignore[arg-type]
        elif inspect.iscoroutinefunction(imported):
            app.middleware("http")(imported)
        else:
            raise ValueError(f"Invalid middleware {middleware}. "
                             f"Must be a function or a class.")

    return app


async def init_app_state(
    engine_client: EngineClient,
    vllm_config: VllmConfig,
    state: State,
    args: Namespace,
) -> None:
    if args.served_model_name is not None:
        served_model_names = args.served_model_name
    else:
        served_model_names = [args.model]

    if args.disable_log_requests:
        request_logger = None
    else:
        request_logger = RequestLogger(max_log_len=args.max_log_len)

    base_model_paths = [
        BaseModelPath(name=name, model_path=args.model)
        for name in served_model_names
    ]

    state.engine_client = engine_client
    state.log_stats = not args.disable_log_stats
    state.vllm_config = vllm_config
    model_config = vllm_config.model_config

    if envs.VLLM_USE_V1:
        supported_tasks = await engine_client \
            .get_supported_tasks()  # type: ignore
    else:
        supported_tasks = model_config.supported_tasks

    logger.info("Supported_tasks: %s", supported_tasks)

    resolved_chat_template = load_chat_template(args.chat_template)
    if resolved_chat_template is not None:
        # Get the tokenizer to check official template
        tokenizer = await engine_client.get_tokenizer()

        if isinstance(tokenizer, MistralTokenizer):
            # The warning is logged in resolve_mistral_chat_template.
            resolved_chat_template = resolve_mistral_chat_template(
                chat_template=resolved_chat_template)
        else:
            hf_chat_template = resolve_hf_chat_template(
                tokenizer=tokenizer,
                chat_template=None,
                tools=None,
                model_config=vllm_config.model_config,
            )

            if hf_chat_template != resolved_chat_template:
                logger.warning(
                    "Using supplied chat template: %s\n"
                    "It is different from official chat template '%s'. "
                    "This discrepancy may lead to performance degradation.",
                    resolved_chat_template, args.model)

    # Merge default_mm_loras into the static lora_modules
    default_mm_loras = (vllm_config.lora_config.default_mm_loras
                        if vllm_config.lora_config is not None else {})

    lora_modules = args.lora_modules
    if default_mm_loras:
        default_mm_lora_paths = [
            LoRAModulePath(
                name=modality,
                path=lora_path,
            ) for modality, lora_path in default_mm_loras.items()
        ]
        if args.lora_modules is None:
            lora_modules = default_mm_lora_paths
        else:
            lora_modules += default_mm_lora_paths

    state.openai_serving_models = OpenAIServingModels(
        engine_client=engine_client,
        model_config=model_config,
        base_model_paths=base_model_paths,
        lora_modules=lora_modules,
    )
    await state.openai_serving_models.init_static_loras()
    state.openai_serving_responses = OpenAIServingResponses(
        engine_client,
        model_config,
        state.openai_serving_models,
        request_logger=request_logger,
        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
        return_tokens_as_token_ids=args.return_tokens_as_token_ids,
        enable_auto_tools=args.enable_auto_tool_choice,
        tool_parser=args.tool_call_parser,
        reasoning_parser=args.reasoning_parser,
        enable_prompt_tokens_details=args.enable_prompt_tokens_details,
        enable_force_include_usage=args.enable_force_include_usage,
    ) if "generate" in supported_tasks else None
    state.openai_serving_chat = OpenAIServingChat(
        engine_client,
        model_config,
        state.openai_serving_models,
        args.response_role,
        request_logger=request_logger,
        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
        return_tokens_as_token_ids=args.return_tokens_as_token_ids,
        enable_auto_tools=args.enable_auto_tool_choice,
        exclude_tools_when_tool_choice_none=args.
        exclude_tools_when_tool_choice_none,
        tool_parser=args.tool_call_parser,
        reasoning_parser=args.reasoning_parser,
        enable_prompt_tokens_details=args.enable_prompt_tokens_details,
        enable_force_include_usage=args.enable_force_include_usage,
    ) if "generate" in supported_tasks else None
    state.openai_serving_completion = OpenAIServingCompletion(
        engine_client,
        model_config,
        state.openai_serving_models,
        request_logger=request_logger,
        return_tokens_as_token_ids=args.return_tokens_as_token_ids,
        enable_prompt_tokens_details=args.enable_prompt_tokens_details,
        enable_force_include_usage=args.enable_force_include_usage,
    ) if "generate" in supported_tasks else None
    state.openai_serving_pooling = OpenAIServingPooling(
        engine_client,
        model_config,
        state.openai_serving_models,
        request_logger=request_logger,
        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
    ) if "encode" in supported_tasks else None
    state.openai_serving_embedding = OpenAIServingEmbedding(
        engine_client,
        model_config,
        state.openai_serving_models,
        request_logger=request_logger,
        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
    ) if "embed" in supported_tasks else None
    state.openai_serving_classification = ServingClassification(
        engine_client,
        model_config,
        state.openai_serving_models,
        request_logger=request_logger,
    ) if "classify" in supported_tasks else None

    enable_serving_reranking = ("classify" in supported_tasks and getattr(
        model_config.hf_config, "num_labels", 0) == 1)
    state.openai_serving_scores = ServingScores(
        engine_client,
        model_config,
        state.openai_serving_models,
        request_logger=request_logger,
    ) if ("embed" in supported_tasks or enable_serving_reranking) else None

    state.openai_serving_tokenization = OpenAIServingTokenization(
        engine_client,
        model_config,
        state.openai_serving_models,
        request_logger=request_logger,
        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
    )
    state.openai_serving_transcription = OpenAIServingTranscription(
        engine_client,
        model_config,
        state.openai_serving_models,
        request_logger=request_logger,
    ) if "transcription" in supported_tasks else None
    state.openai_serving_translation = OpenAIServingTranslation(
        engine_client,
        model_config,
        state.openai_serving_models,
        request_logger=request_logger,
    ) if "transcription" in supported_tasks else None

    state.enable_server_load_tracking = args.enable_server_load_tracking
    state.server_load_metrics = 0


def create_server_socket(addr: tuple[str, int]) -> socket.socket:
    family = socket.AF_INET
    if is_valid_ipv6_address(addr[0]):
        family = socket.AF_INET6

    sock = socket.socket(family=family, type=socket.SOCK_STREAM)
    sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
    sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1)
    sock.bind(addr)

    return sock


def validate_api_server_args(args):
    valid_tool_parses = ToolParserManager.tool_parsers.keys()
    if args.enable_auto_tool_choice \
            and args.tool_call_parser not in valid_tool_parses:
        raise KeyError(f"invalid tool call parser: {args.tool_call_parser} "
                       f"(chose from {{ {','.join(valid_tool_parses)} }})")

    valid_reasoning_parses = ReasoningParserManager.reasoning_parsers.keys()
    if args.reasoning_parser \
        and args.reasoning_parser not in valid_reasoning_parses:
        raise KeyError(
            f"invalid reasoning parser: {args.reasoning_parser} "
            f"(chose from {{ {','.join(valid_reasoning_parses)} }})")


def setup_server(args):
    """Validate API server args, set up signal handler, create socket
    ready to serve."""

    logger.info("vLLM API server version %s", VLLM_VERSION)
    log_non_default_args(args)

    if args.tool_parser_plugin and len(args.tool_parser_plugin) > 3:
        ToolParserManager.import_tool_parser(args.tool_parser_plugin)

    validate_api_server_args(args)

    # workaround to make sure that we bind the port before the engine is set up.
    # This avoids race conditions with ray.
    # see https://github.com/vllm-project/vllm/issues/8204
    sock_addr = (args.host or "", args.port)
    sock = create_server_socket(sock_addr)

    # workaround to avoid footguns where uvicorn drops requests with too
    # many concurrent requests active
    set_ulimit()

    def signal_handler(*_) -> None:
        # Interrupt server on sigterm while initializing
        raise KeyboardInterrupt("terminated")

    signal.signal(signal.SIGTERM, signal_handler)

    addr, port = sock_addr
    is_ssl = args.ssl_keyfile and args.ssl_certfile
    host_part = f"[{addr}]" if is_valid_ipv6_address(
        addr) else addr or "0.0.0.0"
    listen_address = f"http{'s' if is_ssl else ''}://{host_part}:{port}"

    return listen_address, sock


async def run_server(args, **uvicorn_kwargs) -> None:
    """Run a single-worker API server."""

    # Add process-specific prefix to stdout and stderr.
    decorate_logs("APIServer")

    listen_address, sock = setup_server(args)
    await run_server_worker(listen_address, sock, args, **uvicorn_kwargs)


async def run_server_worker(listen_address,
                            sock,
                            args,
                            client_config=None,
                            **uvicorn_kwargs) -> None:
    """Run a single API server worker."""

    if args.tool_parser_plugin and len(args.tool_parser_plugin) > 3:
        ToolParserManager.import_tool_parser(args.tool_parser_plugin)

    server_index = client_config.get("client_index", 0) if client_config else 0
    set_process_title("APIServer", str(server_index))
    # Load logging config for uvicorn if specified
    log_config = load_log_config(args.log_config_file)
    if log_config is not None:
        uvicorn_kwargs['log_config'] = log_config

    async with build_async_engine_client(
            args,
            client_config=client_config,
    ) as engine_client:
        maybe_register_tokenizer_info_endpoint(args)
        app = build_app(args)

        vllm_config = await engine_client.get_vllm_config()
        await init_app_state(engine_client, vllm_config, app.state, args)

        logger.info("Starting vLLM API server %d on %s", server_index,
                    listen_address)
        shutdown_task = await serve_http(
            app,
            sock=sock,
            enable_ssl_refresh=args.enable_ssl_refresh,
            host=args.host,
            port=args.port,
            log_level=args.uvicorn_log_level,
            # NOTE: When the 'disable_uvicorn_access_log' value is True,
            # no access log will be output.
            access_log=not args.disable_uvicorn_access_log,
            timeout_keep_alive=envs.VLLM_HTTP_TIMEOUT_KEEP_ALIVE,
            ssl_keyfile=args.ssl_keyfile,
            ssl_certfile=args.ssl_certfile,
            ssl_ca_certs=args.ssl_ca_certs,
            ssl_cert_reqs=args.ssl_cert_reqs,
            **uvicorn_kwargs,
        )

    # NB: Await server shutdown only after the backend context is exited
    try:
        await shutdown_task
    finally:
        sock.close()


if __name__ == "__main__":
    # NOTE(simon):
    # This section should be in sync with vllm/entrypoints/cli/main.py for CLI
    # entrypoints.
    cli_env_setup()
    parser = FlexibleArgumentParser(
        description="vLLM OpenAI-Compatible RESTful API server.")
    parser = make_arg_parser(parser)
    args = parser.parse_args()
    validate_parsed_serve_args(args)

    uvloop.run(run_server(args))
