import json
import sys
from typing import List, Dict

from fastchat.conversation import Conversation
from fastchat import conversation as conv
from server.model_workers.base import *
from server.model_workers.base import ApiEmbeddingsParams
from configs import logger, log_verbose


class OpenAIWorker(ApiModelWorker):
    """
    支持 OpenAI 格式 API 的 Worker，用于 embedding 和 chat
    """
    DEFAULT_EMBED_MODEL = "text-embedding-ada-002"

    def __init__(
        self,
        *,
        model_names: List[str] = ["openai-api"],
        controller_addr: str = None,
        worker_addr: str = None,
        **kwargs,
    ):
        kwargs.update(model_names=model_names, controller_addr=controller_addr, worker_addr=worker_addr)
        kwargs.setdefault("context_len", 8192)
        super().__init__(**kwargs)

    def do_chat(self, params: ApiChatParams) -> Dict:
        from openai import OpenAI
        params.load_config(self.model_names[0])

        client = OpenAI(
            api_key=params.api_key,
            base_url=params.api_base_url,
        )

        if log_verbose:
            logger.info(f'{self.__class__.__name__}:params: {params}')

        try:
            response = client.chat.completions.create(
                model=params.version or self.model_names[0],
                messages=params.messages,
                temperature=params.temperature,
                max_tokens=params.max_tokens,
                top_p=params.top_p,
                stream=True,
            )
            for chunk in response:
                if chunk.choices:
                    delta = chunk.choices[0].delta
                    if delta.content:
                        yield {
                            "error_code": 0,
                            "text": delta.content,
                        }
        except Exception as e:
            logger.error(f"OpenAI API 请求错误: {e}")
            yield {
                "error_code": 500,
                "text": f"请求错误: {str(e)}",
            }

    def do_embeddings(self, params: ApiEmbeddingsParams) -> Dict:
        from openai import OpenAI
        # embed_texts 里用 worker_class() 默认 model_names 为 ["openai-api"]，会错加载成 openai-api 的 base_url；
        # 在线嵌入应使用 ONLINE_LLM_MODEL 的键（如 bge-m3-api），由调用方写入 params.worker_name。
        params.load_config(params.worker_name or self.model_names[0])

        client = OpenAI(
            api_key=params.api_key,
            base_url=params.api_base_url,
        )

        if log_verbose:
            logger.info(f'{self.__class__.__name__}:params: {params}')

        try:
            # OpenAI embedding API 每次最多处理 2048 个文本，这里分批处理
            result = []
            batch_size = 100
            for i in range(0, len(params.texts), batch_size):
                batch_texts = params.texts[i:i+batch_size]
                response = client.embeddings.create(
                    model=params.embed_model or self.DEFAULT_EMBED_MODEL,
                    input=batch_texts,
                    encoding_format="float",
                )
                embeddings = [item.embedding for item in response.data]
                result.extend(embeddings)

            return {"code": 200, "data": result}
        except Exception as e:
            logger.error(f"OpenAI Embedding API 请求错误: {e}")
            return {
                "code": 500,
                "msg": f"Embedding 请求错误: {str(e)}",
            }

    def make_conv_template(self, conv_template: str = None, model_path: str = None) -> Conversation:
        return conv.Conversation(
            name=self.model_names[0],
            system_message="You are a helpful assistant.",
            messages=[],
            roles=["user", "assistant", "system"],
            sep="\n",
            stop_str="",
        )


if __name__ == "__main__":
    import uvicorn
    from server.utils import MakeFastAPIOffline
    from fastchat.serve.model_worker import app

    worker = OpenAIWorker(
        controller_addr="http://127.0.0.1:20001",
        worker_addr="http://127.0.0.1:20008",
    )
    sys.modules["fastchat.serve.model_worker"].worker = worker
    MakeFastAPIOffline(app)
    uvicorn.run(app, port=20008)