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

from typing import TYPE_CHECKING

from fastapi import FastAPI

if TYPE_CHECKING:
    from argparse import Namespace

    from starlette.datastructures import State

    from vllm.engine.protocol import EngineClient


def register_pooling_api_routers(app: FastAPI):
    from vllm.entrypoints.pooling.classify.api_router import router as classify_router
    from vllm.entrypoints.pooling.embed.api_router import router as embed_router
    from vllm.entrypoints.pooling.pooling.api_router import router as pooling_router
    from vllm.entrypoints.pooling.score.api_router import router as score_router

    app.include_router(classify_router)
    app.include_router(embed_router)
    app.include_router(score_router)
    app.include_router(pooling_router)


async def init_pooling_state(
    engine_client: "EngineClient", state: "State", args: "Namespace"
):
    from vllm.entrypoints.logger import RequestLogger
    from vllm.entrypoints.pooling.classify.serving import ServingClassification
    from vllm.entrypoints.pooling.embed.serving import OpenAIServingEmbedding
    from vllm.entrypoints.pooling.pooling.serving import OpenAIServingPooling
    from vllm.entrypoints.pooling.score.serving import ServingScores
    from vllm.entrypoints.utils import process_chat_template
    from vllm.tasks import POOLING_TASKS

    supported_tasks = await engine_client.get_supported_tasks()

    vllm_config = engine_client.vllm_config

    resolved_chat_template = await process_chat_template(
        args.chat_template, engine_client, vllm_config.model_config
    )

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

    state.openai_serving_pooling = (
        (
            OpenAIServingPooling(
                engine_client,
                state.openai_serving_models,
                supported_tasks=supported_tasks,
                request_logger=request_logger,
                chat_template=resolved_chat_template,
                chat_template_content_format=args.chat_template_content_format,
                trust_request_chat_template=args.trust_request_chat_template,
                log_error_stack=args.log_error_stack,
            )
        )
        if any(task in POOLING_TASKS for task in supported_tasks)
        else None
    )
    state.openai_serving_embedding = (
        OpenAIServingEmbedding(
            engine_client,
            state.openai_serving_models,
            request_logger=request_logger,
            chat_template=resolved_chat_template,
            chat_template_content_format=args.chat_template_content_format,
            trust_request_chat_template=args.trust_request_chat_template,
            log_error_stack=args.log_error_stack,
        )
        if "embed" in supported_tasks
        else None
    )
    state.openai_serving_classification = (
        ServingClassification(
            engine_client,
            state.openai_serving_models,
            request_logger=request_logger,
            chat_template=resolved_chat_template,
            chat_template_content_format=args.chat_template_content_format,
            trust_request_chat_template=args.trust_request_chat_template,
            log_error_stack=args.log_error_stack,
        )
        if "classify" in supported_tasks
        else None
    )
    state.openai_serving_scores = (
        ServingScores(
            engine_client,
            state.openai_serving_models,
            request_logger=request_logger,
            score_template=resolved_chat_template,
            log_error_stack=args.log_error_stack,
        )
        if ("embed" in supported_tasks or "score" in supported_tasks)
        else None
    )
