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

import asyncio
import json
import time
from collections.abc import AsyncGenerator, Callable, Sequence
from functools import partial
from typing import Final, Literal, cast

import jinja2
from fastapi import Request
from typing_extensions import assert_never

from vllm.engine.protocol import EngineClient
from vllm.entrypoints.chat_utils import ChatTemplateContentFormatOption
from vllm.entrypoints.logger import RequestLogger
from vllm.entrypoints.openai.engine.protocol import ErrorResponse, UsageInfo
from vllm.entrypoints.openai.engine.serving import OpenAIServing
from vllm.entrypoints.openai.models.serving import OpenAIServingModels
from vllm.entrypoints.pooling.pooling.protocol import (
    IOProcessorRequest,
    IOProcessorResponse,
    PoolingBytesResponse,
    PoolingChatRequest,
    PoolingCompletionRequest,
    PoolingRequest,
    PoolingResponse,
    PoolingResponseData,
)
from vllm.entrypoints.pooling.utils import (
    encode_pooling_bytes,
    encode_pooling_output_base64,
    encode_pooling_output_float,
)
from vllm.inputs import ProcessorInputs
from vllm.logger import init_logger
from vllm.outputs import PoolingRequestOutput
from vllm.renderers.inputs.preprocess import prompt_to_seq
from vllm.utils.async_utils import merge_async_iterators
from vllm.utils.serial_utils import EmbedDType, EncodingFormat, Endianness

logger = init_logger(__name__)


class OpenAIServingPooling(OpenAIServing):
    def __init__(
        self,
        engine_client: EngineClient,
        models: OpenAIServingModels,
        *,
        request_logger: RequestLogger | None,
        chat_template: str | None,
        chat_template_content_format: ChatTemplateContentFormatOption,
        trust_request_chat_template: bool = False,
        log_error_stack: bool = False,
    ) -> None:
        super().__init__(
            engine_client=engine_client,
            models=models,
            request_logger=request_logger,
            log_error_stack=log_error_stack,
        )

        self.chat_template = chat_template
        self.chat_template_content_format: Final = chat_template_content_format
        self.trust_request_chat_template = trust_request_chat_template

    async def create_pooling(
        self,
        request: PoolingRequest,
        raw_request: Request | None = None,
    ) -> PoolingResponse | IOProcessorResponse | PoolingBytesResponse | ErrorResponse:
        """
        See https://platform.openai.com/docs/api-reference/embeddings/create
        for the API specification. This API mimics the OpenAI Embedding API.
        """
        error_check_ret = await self._check_model(request)
        if error_check_ret is not None:
            return error_check_ret

        model_name = self.models.model_name()

        request_id = f"pool-{self._base_request_id(raw_request)}"
        created_time = int(time.time())

        try:
            lora_request = self._maybe_get_adapters(request)

            if getattr(request, "dimensions", None) is not None:
                return self.create_error_response(
                    "dimensions is currently not supported"
                )

            engine_prompts: Sequence[ProcessorInputs]
            if use_io_processor := isinstance(request, IOProcessorRequest):
                if self.io_processor is None:
                    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."
                    )

                validated_prompt = self.io_processor.parse_data(request.data)

                raw_prompts = await self.io_processor.pre_process_async(
                    prompt=validated_prompt, request_id=request_id
                )
                engine_prompts = await self._preprocess_cmpl(
                    request,
                    prompt_to_seq(raw_prompts),
                )
            elif isinstance(request, PoolingChatRequest):
                error_check_ret = self._validate_chat_template(
                    request_chat_template=request.chat_template,
                    chat_template_kwargs=request.chat_template_kwargs,
                    trust_request_chat_template=self.trust_request_chat_template,
                )
                if error_check_ret is not None:
                    return error_check_ret

                _, engine_prompts = await self._preprocess_chat(
                    request,
                    request.messages,
                    default_template=self.chat_template,
                    default_template_content_format=self.chat_template_content_format,
                    default_template_kwargs=None,
                )
            elif isinstance(request, PoolingCompletionRequest):
                engine_prompts = await self._preprocess_completion(
                    request,
                    prompt_input=request.input,
                    prompt_embeds=None,
                )
            else:
                raise ValueError(f"Unsupported request of type {type(request)}")
        except (ValueError, TypeError, jinja2.TemplateError) as e:
            logger.exception("Error in preprocessing prompt inputs")
            return self.create_error_response(str(e))

        # Schedule the request and get the result generator.
        generators: list[AsyncGenerator[PoolingRequestOutput, None]] = []
        try:
            if use_io_processor:
                assert self.io_processor is not None

                pooling_params = self.io_processor.merge_pooling_params()
                if pooling_params.task is None:
                    pooling_params.task = "plugin"
            else:
                pooling_params = request.to_pooling_params()  # type: ignore

            for i, engine_prompt in enumerate(engine_prompts):
                request_id_item = f"{request_id}-{i}"

                self._log_inputs(
                    request_id_item,
                    engine_prompt,
                    params=pooling_params,
                    lora_request=lora_request,
                )

                trace_headers = (
                    None
                    if raw_request is None
                    else await self._get_trace_headers(raw_request.headers)
                )

                generator = self.engine_client.encode(
                    engine_prompt,
                    pooling_params,
                    request_id_item,
                    lora_request=lora_request,
                    trace_headers=trace_headers,
                    priority=request.priority,
                )

                generators.append(generator)
        except ValueError as e:
            return self.create_error_response(e)

        result_generator = merge_async_iterators(*generators)

        if use_io_processor:
            assert self.io_processor is not None
            output = await self.io_processor.post_process_async(
                result_generator,
                request_id=request_id,
            )

            if callable(
                output_to_response := getattr(
                    self.io_processor, "output_to_response", None
                )
            ):
                logger.warning_once(
                    "`IOProcessor.output_to_response` is deprecated. To ensure "
                    "consistency between offline and online APIs, "
                    "`IOProcessorResponse` will become a transparent wrapper "
                    "around output data from v0.19 onwards.",
                )

                if hasattr(output, "request_id") and output.request_id is None:
                    output.request_id = request_id  # type: ignore

                return output_to_response(output)  # type: ignore

            return IOProcessorResponse(request_id=request_id, data=output)

        assert isinstance(request, (PoolingCompletionRequest, PoolingChatRequest))
        num_prompts = len(engine_prompts)

        # Non-streaming response
        final_res_batch: list[PoolingRequestOutput | None]
        final_res_batch = [None] * num_prompts
        try:
            async for i, res in result_generator:
                final_res_batch[i] = res

            assert all(final_res is not None for final_res in final_res_batch)

            final_res_batch_checked = cast(list[PoolingRequestOutput], final_res_batch)

            response = self.request_output_to_pooling_response(
                final_res_batch_checked,
                request_id,
                created_time,
                model_name,
                request.encoding_format,
                request.embed_dtype,
                request.endianness,
            )
        except asyncio.CancelledError:
            return self.create_error_response("Client disconnected")
        except ValueError as e:
            return self.create_error_response(e)

        return response

    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,
    ) -> PoolingResponse:
        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,
        )

        return PoolingResponse(
            id=request_id,
            created=created_time,
            model=model_name,
            data=items,
            usage=usage,
        )

    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,
    ) -> PoolingBytesResponse:
        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,
                    }
                )
            }
        )

        return PoolingBytesResponse(content=content, headers=headers)

    def request_output_to_pooling_response(
        self,
        final_res_batch: list[PoolingRequestOutput],
        request_id: str,
        created_time: int,
        model_name: str,
        encoding_format: EncodingFormat,
        embed_dtype: EmbedDType,
        endianness: Endianness,
    ) -> PoolingResponse | PoolingBytesResponse:
        if encoding_format == "float" or encoding_format == "base64":
            return self.request_output_to_pooling_json_response(
                final_res_batch,
                request_id,
                created_time,
                model_name,
                encoding_format,
                embed_dtype,
                endianness,
            )

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

        assert_never(encoding_format)
