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


import json
from collections.abc import Callable
from functools import partial
from typing import Literal, cast

from fastapi.responses import JSONResponse, Response, StreamingResponse
from typing_extensions import assert_never

from vllm.entrypoints.openai.engine.protocol import UsageInfo
from vllm.entrypoints.pooling.base.io_processor import PoolingIOProcessor
from vllm.entrypoints.pooling.base.serving import PoolingServingBase
from vllm.entrypoints.pooling.io_processor_factories import init_pooling_io_processors
from vllm.entrypoints.pooling.pooling.protocol import (
    IOProcessorRequest,
    PoolingBytesResponse,
    PoolingRequest,
    PoolingResponse,
    PoolingResponseData,
)
from vllm.entrypoints.pooling.typing import AnyPoolingRequest, PoolingServeContext
from vllm.entrypoints.pooling.utils import (
    encode_pooling_bytes,
    encode_pooling_output_base64,
    encode_pooling_output_float,
    get_json_response_cls,
)
from vllm.logger import init_logger
from vllm.outputs import PoolingRequestOutput
from vllm.tasks import SupportedTask
from vllm.utils.serial_utils import EmbedDType, Endianness

logger = init_logger(__name__)


class ServingPooling(PoolingServingBase):
    request_id_prefix = "pooling"

    def __init__(
        self,
        *args,
        supported_tasks: tuple[SupportedTask, ...],
        **kwargs,
    ):
        super().__init__(*args, **kwargs)

        self.supported_tasks = supported_tasks
        self.pooling_task = self.model_config.get_pooling_task(supported_tasks)
        self.io_processors = init_pooling_io_processors(
            supported_tasks=supported_tasks,
            vllm_config=self.vllm_config,
            renderer=self.renderer,
            chat_template_config=self.chat_template_config,
        )
        self.json_response_cls = get_json_response_cls()

    def get_io_processor(self, request: AnyPoolingRequest) -> PoolingIOProcessor:
        assert isinstance(request, PoolingRequest)
        pooling_task = self._verify_pooling_task(request)
        return self.io_processors[pooling_task]

    def _verify_pooling_task(self, request: PoolingRequest) -> str:
        if getattr(request, "dimensions", None) is not None:
            raise ValueError("dimensions is currently not supported")

        if request.task is None:
            request.task = self.pooling_task

        if isinstance(request, IOProcessorRequest):
            request.task = "plugin"

        assert request.task is not None
        pooling_task = request.task

        # plugin task uses io_processor.parse_request to verify inputs
        if pooling_task != "plugin" and pooling_task != self.pooling_task:
            if pooling_task not in self.io_processors:
                raise ValueError(
                    f"Unsupported task: {pooling_task!r} "
                    f"Supported tasks: {self.supported_tasks}"
                )
            else:
                logger.warning_once(
                    "Pooling multitask support is deprecated and will be removed "
                    "in v0.20. When the default pooling task is not what you want, you "
                    "need to manually specify it via --pooler-config.task %s. ",
                    pooling_task,
                )

        if pooling_task == "plugin" and "plugin" not in self.io_processors:
            raise ValueError(
                "No IOProcessor plugin installed. Please refer "
                "to the documentation and to the "
                "'prithvi_geospatial_mae_io_processor' "
                "offline inference example for more details."
            )

        return pooling_task

    def _build_response(
        self,
        ctx: PoolingServeContext,
    ) -> Response:
        if ctx.response is not None:
            # for IOProcessorResponse
            return self.json_response_cls(content=ctx.response.model_dump())

        encoding_format = ctx.request.encoding_format
        embed_dtype = ctx.request.embed_dtype
        endianness = ctx.request.endianness

        if encoding_format == "float" or encoding_format == "base64":
            return self.request_output_to_pooling_json_response(
                ctx.final_res_batch,
                ctx.request_id,
                ctx.created_time,
                ctx.model_name,
                encoding_format,
                embed_dtype,
                endianness,
            )

        if encoding_format == "bytes" or encoding_format == "bytes_only":
            return self.request_output_to_pooling_bytes_response(
                ctx.final_res_batch,
                ctx.request_id,
                ctx.created_time,
                ctx.model_name,
                encoding_format,
                embed_dtype,
                endianness,
            )

        assert_never(encoding_format)

    def request_output_to_pooling_json_response(
        self,
        final_res_batch: list[PoolingRequestOutput],
        request_id: str,
        created_time: int,
        model_name: str,
        encoding_format: Literal["float", "base64"],
        embed_dtype: EmbedDType,
        endianness: Endianness,
    ) -> JSONResponse:
        encode_fn = cast(
            Callable[[PoolingRequestOutput], list[float] | str],
            (
                encode_pooling_output_float
                if encoding_format == "float"
                else partial(
                    encode_pooling_output_base64,
                    embed_dtype=embed_dtype,
                    endianness=endianness,
                )
            ),
        )

        items: list[PoolingResponseData] = []
        num_prompt_tokens = 0

        for idx, final_res in enumerate(final_res_batch):
            item = PoolingResponseData(
                index=idx,
                data=encode_fn(final_res),
            )
            prompt_token_ids = final_res.prompt_token_ids

            items.append(item)
            num_prompt_tokens += len(prompt_token_ids)

        usage = UsageInfo(
            prompt_tokens=num_prompt_tokens,
            total_tokens=num_prompt_tokens,
        )

        response = PoolingResponse(
            id=request_id,
            created=created_time,
            model=model_name,
            data=items,
            usage=usage,
        )
        return self.json_response_cls(content=response.model_dump())

    def request_output_to_pooling_bytes_response(
        self,
        final_res_batch: list[PoolingRequestOutput],
        request_id: str,
        created_time: int,
        model_name: str,
        encoding_format: Literal["bytes", "bytes_only"],
        embed_dtype: EmbedDType,
        endianness: Endianness,
    ) -> StreamingResponse:
        content, items, usage = encode_pooling_bytes(
            pooling_outputs=final_res_batch,
            embed_dtype=embed_dtype,
            endianness=endianness,
        )

        headers = (
            None
            if encoding_format == "bytes_only"
            else {
                "metadata": json.dumps(
                    {
                        "id": request_id,
                        "created": created_time,
                        "model": model_name,
                        "data": items,
                        "usage": usage,
                    }
                )
            }
        )

        response = PoolingBytesResponse(content=content, headers=headers)

        return StreamingResponse(
            content=response.content,
            headers=response.headers,
            media_type=response.media_type,
        )
