# Copyright 2023-2024 SGLang Team
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Conversion between OpenAI APIs and native SRT APIs"""

import asyncio
import json
import logging
import os
import time
import uuid
from http import HTTPStatus
from typing import Dict, List

from fastapi import HTTPException, Request, UploadFile
from fastapi.responses import ORJSONResponse, StreamingResponse
from pydantic import ValidationError

try:
    from outlines.fsm.json_schema import convert_json_schema_to_str
except ImportError:
    # Before outlines 0.0.47, convert_json_schema_to_str is under
    # outlines.integrations.utils
    from outlines.integrations.utils import convert_json_schema_to_str

from sglang.srt.conversation import (
    Conversation,
    SeparatorStyle,
    chat_template_exists,
    generate_chat_conv,
    generate_embedding_convs,
    register_conv_template,
)
from sglang.srt.function_call_parser import TOOLS_TAG_LIST, FunctionCallParser
from sglang.srt.managers.io_struct import EmbeddingReqInput, GenerateReqInput
from sglang.srt.openai_api.protocol import (
    BatchRequest,
    BatchResponse,
    ChatCompletionRequest,
    ChatCompletionResponse,
    ChatCompletionResponseChoice,
    ChatCompletionResponseStreamChoice,
    ChatCompletionStreamResponse,
    ChatCompletionTokenLogprob,
    ChatMessage,
    ChoiceLogprobs,
    CompletionRequest,
    CompletionResponse,
    CompletionResponseChoice,
    CompletionResponseStreamChoice,
    CompletionStreamResponse,
    DeltaMessage,
    EmbeddingObject,
    EmbeddingRequest,
    EmbeddingResponse,
    ErrorResponse,
    FileDeleteResponse,
    FileRequest,
    FileResponse,
    FunctionResponse,
    LogProbs,
    MultimodalEmbeddingInput,
    ToolCall,
    TopLogprob,
    UsageInfo,
)
from sglang.srt.reasoning_parser import ReasoningParser
from sglang.utils import get_exception_traceback

logger = logging.getLogger(__name__)

chat_template_name = None


class FileMetadata:
    def __init__(self, filename: str, purpose: str):
        self.filename = filename
        self.purpose = purpose


# In-memory storage for batch jobs and files
batch_storage: Dict[str, BatchResponse] = {}
file_id_request: Dict[str, FileMetadata] = {}
file_id_response: Dict[str, FileResponse] = {}
# map file id to file path in SGLang backend
file_id_storage: Dict[str, str] = {}

# backend storage directory
storage_dir = None


def create_error_response(
    message: str,
    err_type: str = "BadRequestError",
    status_code: HTTPStatus = HTTPStatus.BAD_REQUEST,
):
    error = ErrorResponse(message=message, type=err_type, code=status_code.value)
    return ORJSONResponse(content=error.model_dump(), status_code=error.code)


def create_streaming_error_response(
    message: str,
    err_type: str = "BadRequestError",
    status_code: HTTPStatus = HTTPStatus.BAD_REQUEST,
) -> str:
    error = ErrorResponse(message=message, type=err_type, code=status_code.value)
    json_str = json.dumps({"error": error.model_dump()})
    return json_str


def load_chat_template_for_openai_api(tokenizer_manager, chat_template_arg, model_path):
    global chat_template_name

    logger.info(
        f"Use chat template for the OpenAI-compatible API server: {chat_template_arg}"
    )

    if not chat_template_exists(chat_template_arg):
        if not os.path.exists(chat_template_arg):
            raise RuntimeError(
                f"Chat template {chat_template_arg} is not a built-in template name "
                "or a valid chat template file path."
            )
        if chat_template_arg.endswith(".jinja"):
            with open(chat_template_arg, "r") as f:
                chat_template = "".join(f.readlines()).strip("\n")
            tokenizer_manager.tokenizer.chat_template = chat_template.replace(
                "\\n", "\n"
            )
            chat_template_name = None
        else:
            assert chat_template_arg.endswith(
                ".json"
            ), "unrecognized format of chat template file"
            with open(chat_template_arg, "r") as filep:
                template = json.load(filep)
                try:
                    sep_style = SeparatorStyle[template["sep_style"]]
                except KeyError:
                    raise ValueError(
                        f"Unknown separator style: {template['sep_style']}"
                    ) from None
                register_conv_template(
                    Conversation(
                        name=template["name"],
                        system_template=template["system"] + "\n{system_message}",
                        system_message=template.get("system_message", ""),
                        roles=(template["user"], template["assistant"]),
                        sep_style=sep_style,
                        sep=template.get("sep", "\n"),
                        stop_str=template["stop_str"],
                    ),
                    override=True,
                )
            chat_template_name = template["name"]
    else:
        chat_template_name = chat_template_arg

    # Check chat-template
    # TODO:
    # 1. Do not import any code from sglang.lang
    # 2. For VLM, when chat_template_arg is None, set it automatically by guessing from model_path.


async def v1_files_create(
    file: UploadFile, purpose: str, file_storage_path: str = None
):
    try:
        global storage_dir
        if file_storage_path:
            storage_dir = file_storage_path
        # Read the file content
        file_content = await file.read()

        # Create an instance of RequestBody
        request_body = FileRequest(file=file_content, purpose=purpose)

        # Save the file to the sglang_oai_storage directory
        os.makedirs(storage_dir, exist_ok=True)
        file_id = f"backend_input_file-{uuid.uuid4()}"
        filename = f"{file_id}.jsonl"
        file_path = os.path.join(storage_dir, filename)

        with open(file_path, "wb") as f:
            f.write(request_body.file)

        # add info to global file map
        file_id_request[file_id] = FileMetadata(filename=file.filename, purpose=purpose)
        file_id_storage[file_id] = file_path

        # Return the response in the required format
        response = FileResponse(
            id=file_id,
            bytes=len(request_body.file),
            created_at=int(time.time()),
            filename=file.filename,
            purpose=request_body.purpose,
        )
        file_id_response[file_id] = response

        return response
    except ValidationError as e:
        return {"error": "Invalid input", "details": e.errors()}


async def v1_delete_file(file_id: str):
    # Retrieve the file job from the in-memory storage
    file_response = file_id_response.get(file_id)
    if file_response is None:
        raise HTTPException(status_code=404, detail="File not found")
    file_path = file_id_storage.get(file_id)
    if file_path is None:
        raise HTTPException(status_code=404, detail="File not found")
    os.remove(file_path)
    del file_id_response[file_id]
    del file_id_storage[file_id]
    return FileDeleteResponse(id=file_id, deleted=True)


async def v1_batches(tokenizer_manager, raw_request: Request):
    try:
        body = await raw_request.json()

        batch_request = BatchRequest(**body)

        batch_id = f"batch_{uuid.uuid4()}"

        # Create an instance of BatchResponse
        batch_response = BatchResponse(
            id=batch_id,
            endpoint=batch_request.endpoint,
            input_file_id=batch_request.input_file_id,
            completion_window=batch_request.completion_window,
            created_at=int(time.time()),
            metadata=batch_request.metadata,
        )

        batch_storage[batch_id] = batch_response

        # Start processing the batch asynchronously
        asyncio.create_task(process_batch(tokenizer_manager, batch_id, batch_request))

        # Return the initial batch_response
        return batch_response

    except ValidationError as e:
        return {"error": "Invalid input", "details": e.errors()}
    except Exception as e:
        return {"error": str(e)}


async def process_batch(tokenizer_manager, batch_id: str, batch_request: BatchRequest):
    try:
        # Update the batch status to "in_progress"
        batch_storage[batch_id].status = "in_progress"
        batch_storage[batch_id].in_progress_at = int(time.time())

        # Retrieve the input file content
        input_file_request = file_id_request.get(batch_request.input_file_id)
        if not input_file_request:
            raise ValueError("Input file not found")

        # Parse the JSONL file and process each request
        input_file_path = file_id_storage.get(batch_request.input_file_id)
        with open(input_file_path, "r", encoding="utf-8") as f:
            lines = f.readlines()

        total_requests = len(lines)
        completed_requests = 0
        failed_requests = 0

        all_ret = []
        end_point = batch_storage[batch_id].endpoint
        file_request_list = []
        all_requests = []
        request_ids = []
        for line_id, line in enumerate(lines):
            request_data = json.loads(line)
            file_request_list.append(request_data)
            body = request_data["body"]
            request_ids.append(f"{batch_id}-req_{line_id}")

            # Although streaming is supported for standalone completions, it is not supported in
            # batch mode (multiple completions in single request).
            if body.get("stream", False):
                raise ValueError("Streaming requests are not supported in batch mode")

            if end_point == "/v1/chat/completions":
                all_requests.append(ChatCompletionRequest(**body))
            elif end_point == "/v1/completions":
                all_requests.append(CompletionRequest(**body))

        if end_point == "/v1/chat/completions":
            adapted_request, request = v1_chat_generate_request(
                all_requests, tokenizer_manager, request_ids=request_ids
            )
        elif end_point == "/v1/completions":
            adapted_request, request = v1_generate_request(
                all_requests, request_ids=request_ids
            )

        try:
            ret = await tokenizer_manager.generate_request(adapted_request).__anext__()
            if not isinstance(ret, list):
                ret = [ret]
            if end_point == "/v1/chat/completions":
                responses = v1_chat_generate_response(
                    request,
                    ret,
                    to_file=True,
                    cache_report=tokenizer_manager.server_args.enable_cache_report,
                    tool_call_parser=tokenizer_manager.server_args.tool_call_parser,
                )
            else:
                responses = v1_generate_response(
                    request, ret, tokenizer_manager, to_file=True
                )

        except Exception as e:
            logger.error(f"error: {get_exception_traceback()}")
            responses = []
            error_json = {
                "id": f"batch_req_{uuid.uuid4()}",
                "custom_id": request_data.get("custom_id"),
                "response": None,
                "error": {"message": str(e)},
            }
            all_ret.append(error_json)
            failed_requests += len(file_request_list)

        for idx, response in enumerate(responses):
            # the batch_req here can be changed to be named within a batch granularity
            response_json = {
                "id": f"batch_req_{uuid.uuid4()}",
                "custom_id": file_request_list[idx].get("custom_id"),
                "response": response,
                "error": None,
            }
            all_ret.append(response_json)
            completed_requests += 1

        # Write results to a new file
        output_file_id = f"backend_result_file-{uuid.uuid4()}"
        global storage_dir
        output_file_path = os.path.join(storage_dir, f"{output_file_id}.jsonl")
        with open(output_file_path, "w", encoding="utf-8") as f:
            for ret in all_ret:
                f.write(json.dumps(ret) + "\n")

        # Update batch response with output file information
        retrieve_batch = batch_storage[batch_id]
        retrieve_batch.output_file_id = output_file_id
        file_id_storage[output_file_id] = output_file_path
        file_id_response[output_file_id] = FileResponse(
            id=output_file_id,
            bytes=os.path.getsize(output_file_path),
            created_at=int(time.time()),
            filename=f"{output_file_id}.jsonl",
            purpose="batch_result",
        )
        # Update batch status to "completed"
        retrieve_batch.status = "completed"
        retrieve_batch.completed_at = int(time.time())
        retrieve_batch.request_counts = {
            "total": total_requests,
            "completed": completed_requests,
            "failed": failed_requests,
        }

    except Exception as e:
        logger.error(f"error: {e}")
        # Update batch status to "failed"
        retrieve_batch = batch_storage[batch_id]
        retrieve_batch.status = "failed"
        retrieve_batch.failed_at = int(time.time())
        retrieve_batch.errors = {"message": str(e)}


async def v1_retrieve_batch(batch_id: str):
    # Retrieve the batch job from the in-memory storage
    batch_response = batch_storage.get(batch_id)
    if batch_response is None:
        raise HTTPException(status_code=404, detail="Batch not found")

    return batch_response


async def v1_cancel_batch(tokenizer_manager, batch_id: str):
    # Retrieve the batch job from the in-memory storage
    batch_response = batch_storage.get(batch_id)
    if batch_response is None:
        raise HTTPException(status_code=404, detail="Batch not found")

    # Only do cancal when status is "validating" or "in_progress"
    if batch_response.status in ["validating", "in_progress"]:
        # Start cancelling the batch asynchronously
        asyncio.create_task(
            cancel_batch(
                tokenizer_manager=tokenizer_manager,
                batch_id=batch_id,
                input_file_id=batch_response.input_file_id,
            )
        )

        # Update batch status to "cancelling"
        batch_response.status = "cancelling"

        return batch_response
    else:
        raise HTTPException(
            status_code=500,
            detail=f"Current status is {batch_response.status}, no need to cancel",
        )


async def cancel_batch(tokenizer_manager, batch_id: str, input_file_id: str):
    try:
        # Update the batch status to "cancelling"
        batch_storage[batch_id].status = "cancelling"

        # Retrieve the input file content
        input_file_request = file_id_request.get(input_file_id)
        if not input_file_request:
            raise ValueError("Input file not found")

        # Parse the JSONL file and process each request
        input_file_path = file_id_storage.get(input_file_id)
        with open(input_file_path, "r", encoding="utf-8") as f:
            lines = f.readlines()

        # Cancel requests by request_ids
        for line_id in range(len(lines)):
            rid = f"{batch_id}-req_{line_id}"
            tokenizer_manager.abort_request(rid=rid)

        retrieve_batch = batch_storage[batch_id]
        retrieve_batch.status = "cancelled"

    except Exception as e:
        logger.error("error in SGLang:", e)
        # Update batch status to "failed"
        retrieve_batch = batch_storage[batch_id]
        retrieve_batch.status = "failed"
        retrieve_batch.failed_at = int(time.time())
        retrieve_batch.errors = {"message": str(e)}


async def v1_retrieve_file(file_id: str):
    # Retrieve the batch job from the in-memory storage
    file_response = file_id_response.get(file_id)
    if file_response is None:
        raise HTTPException(status_code=404, detail="File not found")
    return file_response


async def v1_retrieve_file_content(file_id: str):
    file_pth = file_id_storage.get(file_id)
    if not file_pth or not os.path.exists(file_pth):
        raise HTTPException(status_code=404, detail="File not found")

    def iter_file():
        with open(file_pth, mode="rb") as file_like:
            yield from file_like

    return StreamingResponse(iter_file(), media_type="application/octet-stream")


def v1_generate_request(
    all_requests: List[CompletionRequest], request_ids: List[str] = None
):
    if len(all_requests) > 1:
        first_prompt_type = type(all_requests[0].prompt)
        for request in all_requests:
            assert (
                type(request.prompt) is first_prompt_type
            ), "All prompts must be of the same type in file input settings"
            if request.n > 1:
                raise ValueError(
                    "Parallel sampling is not supported for completions from files"
                )

    prompts = []
    sampling_params_list = []
    return_logprobs = []
    logprob_start_lens = []
    top_logprobs_nums = []
    lora_paths = []

    for request in all_requests:
        # NOTE: with openai API, the prompt's logprobs are always not computed
        if request.echo and request.logprobs:
            logger.warning(
                "Echo is not compatible with logprobs. "
                "To compute logprobs of input prompt, please use the native /generate API."
            )

        prompts.append(request.prompt)
        lora_paths.append(request.lora_path)
        if request.echo and request.logprobs:
            current_logprob_start_len = 0
        else:
            current_logprob_start_len = -1
        sampling_params_list.append(
            {
                "temperature": request.temperature,
                "max_new_tokens": request.max_tokens,
                "min_new_tokens": request.min_tokens,
                "stop": request.stop,
                "stop_token_ids": request.stop_token_ids,
                "top_p": request.top_p,
                "top_k": request.top_k,
                "min_p": request.min_p,
                "presence_penalty": request.presence_penalty,
                "frequency_penalty": request.frequency_penalty,
                "repetition_penalty": request.repetition_penalty,
                "regex": request.regex,
                "json_schema": request.json_schema,
                "ebnf": request.ebnf,
                "n": request.n,
                "no_stop_trim": request.no_stop_trim,
                "ignore_eos": request.ignore_eos,
                "skip_special_tokens": request.skip_special_tokens,
            }
        )
        return_logprobs.append(request.logprobs is not None)
        logprob_start_lens.append(current_logprob_start_len)
        top_logprobs_nums.append(
            request.logprobs if request.logprobs is not None else 0
        )

    if len(all_requests) == 1:
        if isinstance(prompts[0], str) or isinstance(prompts[0][0], str):
            prompt_kwargs = {"text": prompts[0]}
        else:
            prompt_kwargs = {"input_ids": prompts[0]}
        sampling_params_list = sampling_params_list[0]
        return_logprobs = return_logprobs[0]
        logprob_start_lens = logprob_start_lens[0]
        top_logprobs_nums = top_logprobs_nums[0]
        lora_paths = lora_paths[0]
    else:
        if isinstance(prompts[0], str) or isinstance(prompts[0][0], str):
            prompt_kwargs = {"text": prompts}
        else:
            prompt_kwargs = {"input_ids": prompts}

    adapted_request = GenerateReqInput(
        **prompt_kwargs,
        sampling_params=sampling_params_list,
        return_logprob=return_logprobs,
        top_logprobs_num=top_logprobs_nums,
        logprob_start_len=logprob_start_lens,
        return_text_in_logprobs=True,
        stream=all_requests[0].stream,
        rid=request_ids,
        lora_path=lora_paths,
    )

    return adapted_request, all_requests if len(all_requests) > 1 else all_requests[0]


def v1_generate_response(request, ret, tokenizer_manager, to_file=False):
    choices = []
    echo = False

    if (not isinstance(request, list)) and request.echo:
        # TODO: handle the case propmt is token ids
        if isinstance(request.prompt, list) and isinstance(request.prompt[0], str):
            # for the case of multiple str prompts
            prompts = request.prompt
        elif isinstance(request.prompt, list) and isinstance(request.prompt[0], list):
            # for the case of multiple token ids prompts
            prompts = [
                tokenizer_manager.tokenizer.decode(prompt, skip_special_tokens=True)
                for prompt in request.prompt
            ]
        elif isinstance(request.prompt, list) and isinstance(request.prompt[0], int):
            # for the case of single token ids prompt
            prompts = [
                tokenizer_manager.tokenizer.decode(
                    request.prompt, skip_special_tokens=True
                )
            ]
        else:
            # for the case of single str prompt
            prompts = [request.prompt]
        echo = True

    for idx, ret_item in enumerate(ret):
        text = ret_item["text"]
        if isinstance(request, list) and request[idx].echo:
            echo = True
            text = request[idx].prompt + text
        if echo and not isinstance(request, list):
            prompt_index = idx // request.n
            text = prompts[prompt_index] + text

        logprobs = False
        if isinstance(request, list) and request[idx].logprobs is not None:
            logprobs = True
        elif (not isinstance(request, list)) and request.logprobs is not None:
            logprobs = True
        if logprobs:
            if echo:
                input_token_logprobs = ret_item["meta_info"]["input_token_logprobs"]
                input_top_logprobs = ret_item["meta_info"]["input_top_logprobs"]
            else:
                input_token_logprobs = None
                input_top_logprobs = None

            logprobs = to_openai_style_logprobs(
                input_token_logprobs=input_token_logprobs,
                input_top_logprobs=input_top_logprobs,
                output_token_logprobs=ret_item["meta_info"]["output_token_logprobs"],
                output_top_logprobs=ret_item["meta_info"]["output_top_logprobs"],
            )
        else:
            logprobs = None

        finish_reason = ret_item["meta_info"]["finish_reason"]

        if to_file:
            # to make the choise data json serializable
            choice_data = {
                "index": 0,
                "text": text,
                "logprobs": logprobs,
                "finish_reason": (finish_reason["type"] if finish_reason else ""),
                "matched_stop": (
                    finish_reason["matched"]
                    if finish_reason and "matched" in finish_reason
                    else None
                ),
            }
        else:
            choice_data = CompletionResponseChoice(
                index=idx,
                text=text,
                logprobs=logprobs,
                finish_reason=(finish_reason["type"] if finish_reason else ""),
                matched_stop=(
                    finish_reason["matched"]
                    if finish_reason and "matched" in finish_reason
                    else None
                ),
            )

        choices.append(choice_data)

    if to_file:
        responses = []
        for i, choice in enumerate(choices):
            response = {
                "status_code": 200,
                "request_id": ret[i]["meta_info"]["id"],
                "body": {
                    # remain the same but if needed we can change that
                    "id": ret[i]["meta_info"]["id"],
                    "object": "text_completion",
                    "created": int(time.time()),
                    "model": request[i].model,
                    "choices": choice,
                    "usage": {
                        "prompt_tokens": ret[i]["meta_info"]["prompt_tokens"],
                        "completion_tokens": ret[i]["meta_info"]["completion_tokens"],
                        "total_tokens": ret[i]["meta_info"]["prompt_tokens"]
                        + ret[i]["meta_info"]["completion_tokens"],
                    },
                    "system_fingerprint": None,
                },
            }
            responses.append(response)
        return responses
    else:
        prompt_tokens = sum(
            ret[i]["meta_info"]["prompt_tokens"] for i in range(0, len(ret), request.n)
        )
        completion_tokens = sum(item["meta_info"]["completion_tokens"] for item in ret)
        response = CompletionResponse(
            id=ret[0]["meta_info"]["id"],
            model=request.model,
            choices=choices,
            usage=UsageInfo(
                prompt_tokens=prompt_tokens,
                completion_tokens=completion_tokens,
                total_tokens=prompt_tokens + completion_tokens,
            ),
        )
    return response


async def v1_completions(tokenizer_manager, raw_request: Request):
    request_json = await raw_request.json()
    all_requests = [CompletionRequest(**request_json)]
    adapted_request, request = v1_generate_request(all_requests)

    if adapted_request.stream:

        async def generate_stream_resp():
            stream_buffers = {}
            n_prev_tokens = {}
            prompt_tokens = {}
            completion_tokens = {}
            try:
                async for content in tokenizer_manager.generate_request(
                    adapted_request, raw_request
                ):
                    index = content.get("index", 0)

                    stream_buffer = stream_buffers.get(index, "")
                    n_prev_token = n_prev_tokens.get(index, 0)

                    text = content["text"]
                    prompt_tokens[index] = content["meta_info"]["prompt_tokens"]
                    completion_tokens[index] = content["meta_info"]["completion_tokens"]

                    if not stream_buffer:  # The first chunk
                        if request.echo:
                            if isinstance(request.prompt, str):
                                # for the case of single str prompts
                                prompts = request.prompt
                            elif isinstance(request.prompt, list):
                                if isinstance(request.prompt[0], str):
                                    # for the case of multiple str prompts
                                    prompts = request.prompt[index // request.n]
                                elif isinstance(request.prompt[0], int):
                                    # for the case of single token ids prompt
                                    prompts = tokenizer_manager.tokenizer.decode(
                                        request.prompt, skip_special_tokens=True
                                    )
                                elif isinstance(request.prompt[0], list) and isinstance(
                                    request.prompt[0][0], int
                                ):
                                    # for the case of multiple token ids prompts
                                    prompts = tokenizer_manager.tokenizer.decode(
                                        request.prompt[index // request.n],
                                        skip_special_tokens=True,
                                    )

                            # Prepend prompt in response text.
                            text = prompts + text

                    if request.logprobs is not None:
                        # The first chunk and echo is enabled.
                        if not stream_buffer and request.echo:
                            input_token_logprobs = content["meta_info"][
                                "input_token_logprobs"
                            ]
                            input_top_logprobs = content["meta_info"][
                                "input_top_logprobs"
                            ]
                        else:
                            input_token_logprobs = None
                            input_top_logprobs = None

                        logprobs = to_openai_style_logprobs(
                            input_token_logprobs=input_token_logprobs,
                            input_top_logprobs=input_top_logprobs,
                            output_token_logprobs=content["meta_info"][
                                "output_token_logprobs"
                            ][n_prev_token:],
                            output_top_logprobs=content["meta_info"][
                                "output_top_logprobs"
                            ][n_prev_token:],
                        )
                        n_prev_token = len(
                            content["meta_info"]["output_token_logprobs"]
                        )
                    else:
                        logprobs = None

                    delta = text[len(stream_buffer) :]
                    stream_buffer = stream_buffer + delta
                    finish_reason = content["meta_info"]["finish_reason"]
                    choice_data = CompletionResponseStreamChoice(
                        index=index,
                        text=delta,
                        logprobs=logprobs,
                        finish_reason=(finish_reason["type"] if finish_reason else ""),
                        matched_stop=(
                            finish_reason["matched"]
                            if finish_reason and "matched" in finish_reason
                            else None
                        ),
                    )
                    chunk = CompletionStreamResponse(
                        id=content["meta_info"]["id"],
                        object="text_completion",
                        choices=[choice_data],
                        model=request.model,
                    )

                    stream_buffers[index] = stream_buffer
                    n_prev_tokens[index] = n_prev_token

                    yield f"data: {chunk.model_dump_json()}\n\n"
                if request.stream_options and request.stream_options.include_usage:
                    total_prompt_tokens = sum(
                        tokens
                        for i, tokens in prompt_tokens.items()
                        if i % request.n == 0
                    )
                    total_completion_tokens = sum(
                        tokens for tokens in completion_tokens.values()
                    )
                    usage = UsageInfo(
                        prompt_tokens=total_prompt_tokens,
                        completion_tokens=total_completion_tokens,
                        total_tokens=total_prompt_tokens + total_completion_tokens,
                    )

                    final_usage_chunk = CompletionStreamResponse(
                        id=content["meta_info"]["id"],
                        choices=[],
                        model=request.model,
                        usage=usage,
                    )
                    final_usage_data = final_usage_chunk.model_dump_json(
                        exclude_none=True
                    )
                    yield f"data: {final_usage_data}\n\n"
            except ValueError as e:
                error = create_streaming_error_response(str(e))
                yield f"data: {error}\n\n"
            yield "data: [DONE]\n\n"

        return StreamingResponse(
            generate_stream_resp(),
            media_type="text/event-stream",
            background=tokenizer_manager.create_abort_task(adapted_request),
        )

    # Non-streaming response.
    try:
        ret = await tokenizer_manager.generate_request(
            adapted_request, raw_request
        ).__anext__()
    except ValueError as e:
        return create_error_response(str(e))

    if not isinstance(ret, list):
        ret = [ret]

    response = v1_generate_response(request, ret, tokenizer_manager)
    return response


def v1_chat_generate_request(
    all_requests: List[ChatCompletionRequest],
    tokenizer_manager,
    request_ids: List[str] = None,
):
    input_ids = []
    sampling_params_list = []
    image_data_list = []
    return_logprobs = []
    logprob_start_lens = []
    top_logprobs_nums = []
    modalities_list = []
    lora_paths = []

    # NOTE: with openai API, the prompt's logprobs are always not computed

    for request in all_requests:
        # Prep the data needed for the underlying GenerateReqInput:
        #  - prompt: The full prompt string.
        #  - stop: Custom stop tokens.
        #  - image_data: None or a list of image strings (URLs or base64 strings).
        #    None skips any image processing in GenerateReqInput.
        if not isinstance(request.messages, str):
            # Apply chat template and its stop strings.
            tools = None
            if request.tools and request.tool_choice != "none":
                request.skip_special_tokens = False
                if not isinstance(request.tool_choice, str):
                    tools = [
                        item.function.model_dump()
                        for item in request.tools
                        if item.function.name == request.tool_choice.function.name
                    ]
                else:
                    tools = [item.function.model_dump() for item in request.tools]

            if chat_template_name is None:
                openai_compatible_messages = []
                for message in request.messages:
                    if isinstance(message.content, str):
                        openai_compatible_messages.append(
                            {"role": message.role, "content": message.content}
                        )
                    else:
                        content_list = message.dict()["content"]
                        for content in content_list:
                            if content["type"] == "text":
                                openai_compatible_messages.append(
                                    {"role": message.role, "content": content["text"]}
                                )
                if openai_compatible_messages[-1]["role"] == "assistant":
                    assistant_prefix = openai_compatible_messages[-1]["content"]
                    openai_compatible_messages = openai_compatible_messages[:-1]
                else:
                    assistant_prefix = None

                try:
                    prompt_ids = tokenizer_manager.tokenizer.apply_chat_template(
                        openai_compatible_messages,
                        tokenize=True,
                        add_generation_prompt=True,
                        tools=tools,
                    )
                except:
                    #  This except branch will be triggered when the chosen model
                    #  has a different tools input format that is not compatiable
                    #  with openAI's apply_chat_template tool_call format, like Mistral.
                    tools = [t if "function" in t else {"function": t} for t in tools]
                    prompt_ids = tokenizer_manager.tokenizer.apply_chat_template(
                        openai_compatible_messages,
                        tokenize=True,
                        add_generation_prompt=True,
                        tools=tools,
                    )

                if assistant_prefix:
                    encoded = tokenizer_manager.tokenizer.encode(assistant_prefix)
                    if (
                        encoded
                        and encoded[0] == tokenizer_manager.tokenizer.bos_token_id
                    ):
                        encoded = encoded[1:]
                    prompt_ids += encoded
                stop = request.stop
                image_data = None
                modalities = []
            else:
                conv = generate_chat_conv(request, chat_template_name)
                prompt = conv.get_prompt()
                image_data = conv.image_data
                modalities = conv.modalities
                stop = conv.stop_str or []
                if request.stop:
                    if isinstance(request.stop, str):
                        stop.append(request.stop)
                    else:
                        stop.extend(request.stop)
                prompt_ids = tokenizer_manager.tokenizer.encode(prompt)
        else:
            # Use the raw prompt and stop strings if the messages is already a string.
            prompt_ids = request.messages
            stop = request.stop
            image_data = None
            modalities = []
        input_ids.append(prompt_ids)
        return_logprobs.append(request.logprobs)
        logprob_start_lens.append(-1)
        top_logprobs_nums.append(request.top_logprobs or 0)
        lora_paths.append(request.lora_path)

        sampling_params = {
            "temperature": request.temperature,
            "max_new_tokens": request.max_tokens,
            "min_new_tokens": request.min_tokens,
            "stop": stop,
            "stop_token_ids": request.stop_token_ids,
            "top_p": request.top_p,
            "top_k": request.top_k,
            "min_p": request.min_p,
            "presence_penalty": request.presence_penalty,
            "frequency_penalty": request.frequency_penalty,
            "repetition_penalty": request.repetition_penalty,
            "regex": request.regex,
            "ebnf": request.ebnf,
            "n": request.n,
            "no_stop_trim": request.no_stop_trim,
            "ignore_eos": request.ignore_eos,
            "skip_special_tokens": request.skip_special_tokens,
        }

        if request.response_format and request.response_format.type == "json_schema":
            sampling_params["json_schema"] = convert_json_schema_to_str(
                request.response_format.json_schema.schema_
            )
        elif (
            request.response_format and request.response_format.type == "structural_tag"
        ):
            sampling_params["structural_tag"] = convert_json_schema_to_str(
                request.response_format.model_dump(by_alias=True)
            )
        sampling_params_list.append(sampling_params)

        image_data_list.append(image_data)
        modalities_list.append(modalities)
    if len(all_requests) == 1:
        if isinstance(input_ids[0], str):
            prompt_kwargs = {"text": input_ids[0]}
        else:
            prompt_kwargs = {"input_ids": input_ids[0]}
        sampling_params_list = sampling_params_list[0]
        image_data_list = image_data_list[0]
        return_logprobs = return_logprobs[0]
        logprob_start_lens = logprob_start_lens[0]
        top_logprobs_nums = top_logprobs_nums[0]
        modalities_list = modalities_list[0]
        lora_paths = lora_paths[0]
    else:
        if isinstance(input_ids[0], str):
            prompt_kwargs = {"text": input_ids}
        else:
            prompt_kwargs = {"input_ids": input_ids}

    adapted_request = GenerateReqInput(
        **prompt_kwargs,
        image_data=image_data_list,
        sampling_params=sampling_params_list,
        return_logprob=return_logprobs,
        logprob_start_len=logprob_start_lens,
        top_logprobs_num=top_logprobs_nums,
        stream=all_requests[0].stream,
        return_text_in_logprobs=True,
        rid=request_ids,
        modalities=modalities_list,
        lora_path=lora_paths,
    )

    return adapted_request, all_requests if len(all_requests) > 1 else all_requests[0]


def v1_chat_generate_response(
    request,
    ret,
    to_file=False,
    cache_report=False,
    tool_call_parser=None,
    reasoning_parser=None,
):
    choices = []

    for idx, ret_item in enumerate(ret):
        logprobs = False
        if isinstance(request, list) and request[idx].logprobs:
            logprobs = True
        elif (not isinstance(request, list)) and request.logprobs:
            logprobs = True
        if logprobs:
            logprobs = to_openai_style_logprobs(
                output_token_logprobs=ret_item["meta_info"]["output_token_logprobs"],
                output_top_logprobs=ret_item["meta_info"]["output_top_logprobs"],
            )
            token_logprobs = []
            for token_idx, (token, logprob) in enumerate(
                zip(logprobs.tokens, logprobs.token_logprobs)
            ):
                token_bytes = list(token.encode("utf-8"))
                top_logprobs = []
                if logprobs.top_logprobs:
                    for top_token, top_logprob in logprobs.top_logprobs[
                        token_idx
                    ].items():
                        top_token_bytes = list(top_token.encode("utf-8"))
                        top_logprobs.append(
                            TopLogprob(
                                token=top_token,
                                bytes=top_token_bytes,
                                logprob=top_logprob,
                            )
                        )
                token_logprobs.append(
                    ChatCompletionTokenLogprob(
                        token=token,
                        bytes=token_bytes,
                        logprob=logprob,
                        top_logprobs=top_logprobs,
                    )
                )

            choice_logprobs = ChoiceLogprobs(content=token_logprobs)
        else:
            choice_logprobs = None

        finish_reason = ret_item["meta_info"]["finish_reason"]

        tool_calls = None
        text = ret_item["text"]

        if isinstance(request, list):
            tool_choice = request[idx].tool_choice
            tools = request[idx].tools
            separate_reasoning = request[idx].separate_reasoning
        else:
            tool_choice = request.tool_choice
            tools = request.tools
            separate_reasoning = request.separate_reasoning

        if reasoning_parser and separate_reasoning:
            try:
                parser = ReasoningParser(
                    model_type=reasoning_parser, stream_reasoning=False
                )
                reasoning_text, text = parser.parse_non_stream(text)
            except Exception as e:
                logger.error(f"Exception: {e}")
                return create_error_response(
                    HTTPStatus.BAD_REQUEST,
                    "Failed to parse reasoning related info to json format!",
                )
        else:
            reasoning_text = None

        if tool_choice != "none" and any([i in text for i in TOOLS_TAG_LIST]):
            if finish_reason == "stop":
                finish_reason = "tool_calls"
            try:
                parser = FunctionCallParser(tools, tool_call_parser)
                full_normal_text, call_info_list = parser.parse_non_stream(text)
                tool_calls = [
                    ToolCall(
                        id=str(call_info.tool_index),
                        function=FunctionResponse(
                            name=call_info.name, arguments=call_info.parameters
                        ),
                    )
                    for call_info in call_info_list
                ]
            except Exception as e:
                logger.error(f"Exception: {e}")
                return create_error_response(
                    HTTPStatus.BAD_REQUEST,
                    "Failed to parse fc related info to json format!",
                )

        if to_file:
            # to make the choice data json serializable
            choice_data = {
                "index": 0,
                "message": {
                    "role": "assistant",
                    "content": text if tool_calls is None else None,
                    "tool_calls": tool_calls,
                    "reasoning_content": reasoning_text,
                },
                "logprobs": choice_logprobs.model_dump() if choice_logprobs else None,
                "finish_reason": (finish_reason["type"] if finish_reason else ""),
                "matched_stop": (
                    finish_reason["matched"]
                    if finish_reason and "matched" in finish_reason
                    else None
                ),
            }
        else:
            choice_data = ChatCompletionResponseChoice(
                index=idx,
                message=ChatMessage(
                    role="assistant",
                    content=text if tool_calls is None else None,
                    tool_calls=tool_calls,
                    reasoning_content=reasoning_text,
                ),
                logprobs=choice_logprobs,
                finish_reason=(finish_reason["type"] if finish_reason else ""),
                matched_stop=(
                    finish_reason["matched"]
                    if finish_reason and "matched" in finish_reason
                    else None
                ),
            )

        choices.append(choice_data)

    if to_file:
        responses = []

        for i, choice in enumerate(choices):
            response = {
                "status_code": 200,
                "request_id": ret[i]["meta_info"]["id"],
                "body": {
                    # remain the same but if needed we can change that
                    "id": ret[i]["meta_info"]["id"],
                    "object": "chat.completion",
                    "created": int(time.time()),
                    "model": request[i].model,
                    "choices": choice,
                    "usage": {
                        "prompt_tokens": ret[i]["meta_info"]["prompt_tokens"],
                        "completion_tokens": ret[i]["meta_info"]["completion_tokens"],
                        "total_tokens": ret[i]["meta_info"]["prompt_tokens"]
                        + ret[i]["meta_info"]["completion_tokens"],
                    },
                    "system_fingerprint": None,
                },
            }
            responses.append(response)
        return responses
    else:
        prompt_tokens = sum(
            ret[i]["meta_info"]["prompt_tokens"] for i in range(0, len(ret), request.n)
        )
        completion_tokens = sum(item["meta_info"]["completion_tokens"] for item in ret)
        cached_tokens = sum(item["meta_info"].get("cached_tokens", 0) for item in ret)
        response = ChatCompletionResponse(
            id=ret[0]["meta_info"]["id"],
            model=request.model,
            choices=choices,
            usage=UsageInfo(
                prompt_tokens=prompt_tokens,
                completion_tokens=completion_tokens,
                total_tokens=prompt_tokens + completion_tokens,
                prompt_tokens_details=(
                    {"cached_tokens": cached_tokens} if cache_report else None
                ),
            ),
        )
        return response


async def v1_chat_completions(tokenizer_manager, raw_request: Request):
    request_json = await raw_request.json()
    all_requests = [ChatCompletionRequest(**request_json)]
    adapted_request, request = v1_chat_generate_request(all_requests, tokenizer_manager)

    if adapted_request.stream:
        parser_dict = {}
        reasoning_parser_dict = {}

        async def generate_stream_resp():
            is_firsts = {}
            stream_buffers = {}
            n_prev_tokens = {}
            prompt_tokens = {}
            completion_tokens = {}
            try:
                async for content in tokenizer_manager.generate_request(
                    adapted_request, raw_request
                ):
                    index = content.get("index", 0)
                    text = content["text"]

                    is_first = is_firsts.get(index, True)
                    stream_buffer = stream_buffers.get(index, "")
                    n_prev_token = n_prev_tokens.get(index, 0)

                    prompt_tokens[index] = content["meta_info"]["prompt_tokens"]
                    completion_tokens[index] = content["meta_info"]["completion_tokens"]
                    if request.logprobs:
                        logprobs = to_openai_style_logprobs(
                            output_token_logprobs=content["meta_info"][
                                "output_token_logprobs"
                            ][n_prev_token:],
                            output_top_logprobs=content["meta_info"][
                                "output_top_logprobs"
                            ][n_prev_token:],
                        )

                        n_prev_token = len(
                            content["meta_info"]["output_token_logprobs"]
                        )
                        token_logprobs = []
                        for token, logprob in zip(
                            logprobs.tokens, logprobs.token_logprobs
                        ):
                            token_bytes = list(token.encode("utf-8"))
                            top_logprobs = []
                            if logprobs.top_logprobs:
                                for top_token, top_logprob in logprobs.top_logprobs[
                                    0
                                ].items():
                                    top_token_bytes = list(top_token.encode("utf-8"))
                                    top_logprobs.append(
                                        TopLogprob(
                                            token=top_token,
                                            bytes=top_token_bytes,
                                            logprob=top_logprob,
                                        )
                                    )
                            token_logprobs.append(
                                ChatCompletionTokenLogprob(
                                    token=token,
                                    bytes=token_bytes,
                                    logprob=logprob,
                                    top_logprobs=top_logprobs,
                                )
                            )

                        choice_logprobs = ChoiceLogprobs(content=token_logprobs)

                    else:
                        choice_logprobs = None

                    finish_reason = content["meta_info"]["finish_reason"]
                    finish_reason_type = (
                        finish_reason["type"] if finish_reason else None
                    )

                    if is_first:
                        # First chunk with role
                        is_first = False
                        if (
                            tokenizer_manager.server_args.reasoning_parser
                            and request.separate_reasoning
                        ):
                            delta = DeltaMessage(role="assistant", reasoning_content="")
                        else:
                            delta = DeltaMessage(role="assistant", content="")
                        choice_data = ChatCompletionResponseStreamChoice(
                            index=index,
                            delta=delta,
                            finish_reason=(
                                None
                                if finish_reason_type and len(finish_reason_type) == 0
                                else finish_reason_type
                            ),
                            matched_stop=(
                                finish_reason["matched"]
                                if finish_reason and "matched" in finish_reason
                                else None
                            ),
                            logprobs=choice_logprobs,
                        )
                        chunk = ChatCompletionStreamResponse(
                            id=content["meta_info"]["id"],
                            choices=[choice_data],
                            model=request.model,
                        )
                        yield f"data: {chunk.model_dump_json()}\n\n"

                    text = content["text"]
                    delta = text[len(stream_buffer) :]
                    new_stream_buffer = stream_buffer + delta

                    if (
                        tokenizer_manager.server_args.reasoning_parser
                        and request.separate_reasoning
                    ):
                        if index not in reasoning_parser_dict:
                            reasoning_parser_dict[index] = ReasoningParser(
                                tokenizer_manager.server_args.reasoning_parser,
                                request.stream_reasoning,
                            )
                        reasoning_parser = reasoning_parser_dict[index]
                        reasoning_text, delta = reasoning_parser.parse_stream_chunk(
                            delta
                        )
                        if reasoning_text:
                            choice_data = ChatCompletionResponseStreamChoice(
                                index=index,
                                delta=DeltaMessage(reasoning_content=reasoning_text),
                                finish_reason=(
                                    None
                                    if finish_reason_type
                                    and len(finish_reason_type) == 0
                                    else finish_reason_type
                                ),
                            )
                            chunk = ChatCompletionStreamResponse(
                                id=content["meta_info"]["id"],
                                choices=[choice_data],
                                model=request.model,
                            )
                            yield f"data: {chunk.model_dump_json()}\n\n"
                        if (delta and len(delta) == 0) or not delta:
                            stream_buffers[index] = new_stream_buffer
                            is_firsts[index] = is_first
                            continue

                    if request.tool_choice != "none" and request.tools:
                        if index not in parser_dict:
                            parser_dict[index] = FunctionCallParser(
                                tools=request.tools,
                                tool_call_parser=tokenizer_manager.server_args.tool_call_parser,
                            )
                        parser = parser_dict[index]

                        # parse_increment => returns (normal_text, calls)
                        normal_text, calls = parser.parse_stream_chunk(delta)

                        # 1) if there's normal_text, output it as normal content
                        if normal_text:
                            choice_data = ChatCompletionResponseStreamChoice(
                                index=index,
                                delta=DeltaMessage(content=normal_text),
                                finish_reason=(
                                    None
                                    if finish_reason_type
                                    and len(finish_reason_type) == 0
                                    else finish_reason_type
                                ),
                            )
                            chunk = ChatCompletionStreamResponse(
                                id=content["meta_info"]["id"],
                                choices=[choice_data],
                                model=request.model,
                            )
                            yield f"data: {chunk.model_dump_json()}\n\n"

                        # 2) if we found calls, we output them as separate chunk(s)
                        for call_item in calls:
                            # transform call_item -> FunctionResponse + ToolCall

                            if (
                                content["meta_info"]["finish_reason"]
                                and content["meta_info"]["finish_reason"]["type"]
                                == "stop"
                            ):
                                latest_delta_len = 0
                                if isinstance(call_item.parameters, str):
                                    latest_delta_len = len(call_item.parameters)

                                expected_call = json.dumps(
                                    parser.multi_format_parser.detectors[0]
                                    .prev_tool_call_arr[index]
                                    .get("arguments", {}),
                                    ensure_ascii=False,
                                )
                                actual_call = parser.multi_format_parser.detectors[
                                    0
                                ].streamed_args_for_tool[index]
                                if latest_delta_len > 0:
                                    actual_call = actual_call[:-latest_delta_len]
                                remaining_call = expected_call.replace(
                                    actual_call, "", 1
                                )
                                call_item.parameters = remaining_call

                            tool_call = ToolCall(
                                id=str(call_item.tool_index),
                                function=FunctionResponse(
                                    name=call_item.name,
                                    arguments=call_item.parameters,
                                ),
                            )
                            choice_data = ChatCompletionResponseStreamChoice(
                                index=index,
                                delta=DeltaMessage(
                                    role="assistant", tool_calls=[tool_call]
                                ),
                                finish_reason="tool_call",
                            )
                            chunk = ChatCompletionStreamResponse(
                                id=content["meta_info"]["id"],
                                choices=[choice_data],
                                model=request.model,
                            )
                            yield f"data: {chunk.model_dump_json()}\n\n"

                        stream_buffers[index] = new_stream_buffer
                        is_firsts[index] = is_first

                    else:
                        # No tool calls => just treat this as normal text
                        choice_data = ChatCompletionResponseStreamChoice(
                            index=index,
                            delta=DeltaMessage(content=delta),
                            finish_reason=(
                                None
                                if finish_reason_type and len(finish_reason_type) == 0
                                else finish_reason_type
                            ),
                            matched_stop=(
                                finish_reason["matched"]
                                if finish_reason and "matched" in finish_reason
                                else None
                            ),
                            logprobs=choice_logprobs,
                        )
                        chunk = ChatCompletionStreamResponse(
                            id=content["meta_info"]["id"],
                            choices=[choice_data],
                            model=request.model,
                        )
                        yield f"data: {chunk.model_dump_json()}\n\n"
                        stream_buffers[index] = new_stream_buffer
                        is_firsts[index] = is_first
                if request.stream_options and request.stream_options.include_usage:
                    total_prompt_tokens = sum(
                        tokens
                        for i, tokens in prompt_tokens.items()
                        if i % request.n == 0
                    )
                    total_completion_tokens = sum(
                        tokens for tokens in completion_tokens.values()
                    )
                    usage = UsageInfo(
                        prompt_tokens=total_prompt_tokens,
                        completion_tokens=total_completion_tokens,
                        total_tokens=total_prompt_tokens + total_completion_tokens,
                    )

                    final_usage_chunk = ChatCompletionStreamResponse(
                        id=content["meta_info"]["id"],
                        choices=[],
                        model=request.model,
                        usage=usage,
                    )
                    final_usage_data = final_usage_chunk.model_dump_json(
                        exclude_none=True
                    )
                    yield f"data: {final_usage_data}\n\n"
            except ValueError as e:
                error = create_streaming_error_response(str(e))
                yield f"data: {error}\n\n"
            yield "data: [DONE]\n\n"

        return StreamingResponse(
            generate_stream_resp(),
            media_type="text/event-stream",
            background=tokenizer_manager.create_abort_task(adapted_request),
        )

    # Non-streaming response.
    try:
        ret = await tokenizer_manager.generate_request(
            adapted_request, raw_request
        ).__anext__()
    except ValueError as e:
        return create_error_response(str(e))
    if not isinstance(ret, list):
        ret = [ret]

    response = v1_chat_generate_response(
        request,
        ret,
        cache_report=tokenizer_manager.server_args.enable_cache_report,
        tool_call_parser=tokenizer_manager.server_args.tool_call_parser,
        reasoning_parser=tokenizer_manager.server_args.reasoning_parser,
    )

    return response


def v1_embedding_request(all_requests, tokenizer_manager):
    prompts = []
    sampling_params_list = []
    first_prompt_type = type(all_requests[0].input)

    for request in all_requests:
        prompt = request.input
        assert (
            type(prompt) is first_prompt_type
        ), "All prompts must be of the same type in file input settings"
        prompts.append(prompt)

    if len(all_requests) == 1:
        prompt = prompts[0]
        if isinstance(prompt, str) or isinstance(prompt[0], str):
            prompt_kwargs = {"text": prompt}
        elif isinstance(prompt, list) and isinstance(
            prompt[0], MultimodalEmbeddingInput
        ):
            assert (
                chat_template_name is not None
            ), "chat_template_name is required for multimodal inputs"
            texts = []
            images = []
            for item in prompt:
                texts.append(item.text if item.text is not None else None)
                images.append(item.image if item.image is not None else None)
            convs = generate_embedding_convs(texts, images, chat_template_name)
            generate_prompts = []
            for conv in convs:
                generate_prompts.append(conv.get_prompt())
            if len(generate_prompts) == 1:
                prompt_kwargs = {"text": generate_prompts[0], "image_data": images[0]}
            else:
                prompt_kwargs = {"text": generate_prompts, "image_data": images}
        else:
            prompt_kwargs = {"input_ids": prompt}
    else:
        if isinstance(prompts[0], str) or isinstance(prompts[0][0], str):
            prompt_kwargs = {"text": prompts}
        elif isinstance(prompts[0], list) and isinstance(
            prompts[0][0], MultimodalEmbeddingInput
        ):
            # TODO: multiple requests
            raise NotImplementedError(
                "Multiple requests with multimodal inputs are not supported yet"
            )
        else:
            prompt_kwargs = {"input_ids": prompts}

    adapted_request = EmbeddingReqInput(
        **prompt_kwargs,
    )

    if len(all_requests) == 1:
        return adapted_request, all_requests[0]
    return adapted_request, all_requests


def v1_embedding_response(ret, model_path, to_file=False):
    embedding_objects = []
    prompt_tokens = 0
    for idx, ret_item in enumerate(ret):
        embedding_objects.append(
            EmbeddingObject(
                embedding=ret[idx]["embedding"],
                index=idx,
            )
        )
        prompt_tokens += ret[idx]["meta_info"]["prompt_tokens"]

    return EmbeddingResponse(
        data=embedding_objects,
        model=model_path,
        usage=UsageInfo(
            prompt_tokens=prompt_tokens,
            total_tokens=prompt_tokens,
        ),
    )


async def v1_embeddings(tokenizer_manager, raw_request: Request):
    request_json = await raw_request.json()
    all_requests = [EmbeddingRequest(**request_json)]
    adapted_request, request = v1_embedding_request(all_requests, tokenizer_manager)

    try:
        ret = await tokenizer_manager.generate_request(
            adapted_request, raw_request
        ).__anext__()
    except ValueError as e:
        return create_error_response(str(e))

    if not isinstance(ret, list):
        ret = [ret]

    response = v1_embedding_response(ret, tokenizer_manager.model_path)

    return response


def to_openai_style_logprobs(
    input_token_logprobs=None,
    output_token_logprobs=None,
    input_top_logprobs=None,
    output_top_logprobs=None,
):
    ret_logprobs = LogProbs()

    def append_token_logprobs(token_logprobs):
        for logprob, _, token_text in token_logprobs:
            ret_logprobs.tokens.append(token_text)
            ret_logprobs.token_logprobs.append(logprob)

            # Not supported yet
            ret_logprobs.text_offset.append(-1)

    def append_top_logprobs(top_logprobs):
        for tokens in top_logprobs:
            if tokens is not None:
                ret_logprobs.top_logprobs.append(
                    {token[2]: token[0] for token in tokens}
                )
            else:
                ret_logprobs.top_logprobs.append(None)

    if input_token_logprobs is not None:
        append_token_logprobs(input_token_logprobs)
    if output_token_logprobs is not None:
        append_token_logprobs(output_token_logprobs)
    if input_top_logprobs is not None:
        append_top_logprobs(input_top_logprobs)
    if output_top_logprobs is not None:
        append_top_logprobs(output_top_logprobs)

    return ret_logprobs
