# coding=utf-8
import time
import torch
import uvicorn
from pydantic import BaseModel, Field
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from contextlib import asynccontextmanager
from typing import Any, Dict, List, Literal, Optional, Union
from transformers import AutoTokenizer, AutoModel
from sse_starlette.sse import ServerSentEvent, EventSourceResponse
from fastapi import Depends, HTTPException, Request
from starlette.status import HTTP_401_UNAUTHORIZED
import argparse


@asynccontextmanager
async def lifespan(app: FastAPI): # collects GPU memory
    yield
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
        torch.cuda.ipc_collect()


app = FastAPI(lifespan=lifespan)

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


class ChatMessage(BaseModel):
    role: Literal["user", "assistant", "system"]
    content: str


class DeltaMessage(BaseModel):
    role: Optional[Literal["user", "assistant", "system"]] = None
    content: Optional[str] = None


class ChatCompletionRequest(BaseModel):
    model: str
    messages: List[ChatMessage]
    temperature: Optional[float] = None
    top_p: Optional[float] = None
    max_length: Optional[int] = None
    stream: Optional[bool] = False


class ChatCompletionResponseChoice(BaseModel):
    index: int
    message: ChatMessage
    finish_reason: Literal["stop", "length"]


class ChatCompletionResponseStreamChoice(BaseModel):
    index: int
    delta: DeltaMessage
    finish_reason: Optional[Literal["stop", "length"]]


class ChatCompletionResponse(BaseModel):
    model: str
    object: Literal["chat.completion", "chat.completion.chunk"]
    choices: List[Union[ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice]]
    created: Optional[int] = Field(default_factory=lambda: int(time.time()))

async def verify_token(request: Request):
    auth_header = request.headers.get('Authorization')
    if auth_header:
        token_type, _, token = auth_header.partition(' ')
        if token_type.lower() == "bearer" and token == "sk-aaabbbcccdddeeefffggghhhiiijjjkkk": # 这里配置你的token
            return True
    raise HTTPException(
        status_code=HTTP_401_UNAUTHORIZED,
        detail="Invalid authorization credentials",
    )


@app.post("/v1/chat/completions", response_model=ChatCompletionResponse)
async def create_chat_completion(request: ChatCompletionRequest, token: bool = Depends(verify_token)):
    global model, tokenizer

    if request.messages[-1].role != "user":
        raise HTTPException(status_code=400, detail="Invalid request")
    query = request.messages[-1].content

    prev_messages = request.messages[:-1]
    if len(prev_messages) > 0 and prev_messages[0].role == "system":
        query = prev_messages.pop(0).content + query

    history = []
    if len(prev_messages) % 2 == 0:
        for i in range(0, len(prev_messages), 2):
            if prev_messages[i].role == "user" and prev_messages[i+1].role == "assistant":
                history.append([prev_messages[i].content, prev_messages[i+1].content])

    if request.stream:
        generate = predict(query, history, request.model)
        return EventSourceResponse(generate, media_type="text/event-stream")

    response, _ = model.chat(tokenizer, query, history=history)
    choice_data = ChatCompletionResponseChoice(
        index=0,
        message=ChatMessage(role="assistant", content=response),
        finish_reason="stop"
    )

    return ChatCompletionResponse(model=request.model, choices=[choice_data], object="chat.completion")


async def predict(query: str, history: List[List[str]], model_id: str):
    global model, tokenizer

    choice_data = ChatCompletionResponseStreamChoice(
        index=0,
        delta=DeltaMessage(role="assistant"),
        finish_reason=None
    )
    chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
    yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))

    current_length = 0

    for new_response, _ in model.stream_chat(tokenizer, query, history):
        if len(new_response) == current_length:
            continue

        new_text = new_response[current_length:]
        current_length = len(new_response)

        choice_data = ChatCompletionResponseStreamChoice(
            index=0,
            delta=DeltaMessage(content=new_text),
            finish_reason=None
        )
        chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
        yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))


    choice_data = ChatCompletionResponseStreamChoice(
        index=0,
        delta=DeltaMessage(),
        finish_reason="stop"
    )
    chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
    yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
    yield '[DONE]'



if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_name", default="16", type=str, help="Model name")
    args = parser.parse_args()

    model_dict = {
        "4": "THUDM/chatglm2-6b-int4",
        "8": "THUDM/chatglm2-6b-int8",
        "16": "THUDM/chatglm2-6b"
    }

    model_name = model_dict.get(args.model_name, "THUDM/chatglm2-6b")

    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
    model = AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda()
    model.eval()

    uvicorn.run(app, host='0.0.0.0', port=6006, workers=1)