import argparse
import markdown2
import os
import sys
import uvicorn

from pathlib import Path
from typing import Union

from fastapi import FastAPI, Depends
from fastapi.responses import HTMLResponse
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
from pydantic import BaseModel, Field
from sse_starlette.sse import EventSourceResponse, ServerSentEvent
from tclogger import logger

from constants.models import AVAILABLE_MODELS_DICTS
from constants.envs import CONFIG

from messagers.message_composer import MessageComposer
from mocks.stream_chat_mocker import stream_chat_mock
from networks.huggingface_streamer import HuggingfaceStreamer
from networks.openai_streamer import OpenaiStreamer


class ChatAPIApp:
    def __init__(self):
        self.app = FastAPI(
            docs_url="/",
            title=CONFIG["app_name"],
            swagger_ui_parameters={"defaultModelsExpandDepth": -1},
            version=CONFIG["version"],
        )
        self.setup_routes()

    def get_available_models(self):
        return {"object": "list", "data": AVAILABLE_MODELS_DICTS}

    def extract_api_key(
        credentials: HTTPAuthorizationCredentials = Depends(
            HTTPBearer(auto_error=False)
        ),
    ):
        api_key = None
        if credentials:
            api_key = credentials.credentials
        else:
            api_key = os.getenv("HF_TOKEN")

        if api_key:
            if api_key.startswith("hf_"):
                return api_key
            else:
                logger.warn(f"Invalid HF Token!")
        else:
            logger.warn("Not provide HF Token!")
        return None

    class ChatCompletionsPostItem(BaseModel):
        model: str = Field(
            default="mixtral-8x7b",
            description="(str) `mixtral-8x7b`",
        )
        messages: list = Field(
            default=[{"role": "user", "content": "Hello, who are you?"}],
            description="(list) Messages",
        )
        temperature: Union[float, None] = Field(
            default=0.5,
            description="(float) Temperature",
        )
        top_p: Union[float, None] = Field(
            default=0.95,
            description="(float) top p",
        )
        max_tokens: Union[int, None] = Field(
            default=-1,
            description="(int) Max tokens",
        )
        use_cache: bool = Field(
            default=False,
            description="(bool) Use cache",
        )
        stream: bool = Field(
            default=True,
            description="(bool) Stream",
        )

    def chat_completions(
        self, item: ChatCompletionsPostItem, api_key: str = Depends(extract_api_key)
    ):
        if item.model == "gpt-3.5-turbo":
            streamer = OpenaiStreamer()
            stream_response = streamer.chat_response(messages=item.messages)
        else:
            streamer = HuggingfaceStreamer(model=item.model)
            composer = MessageComposer(model=item.model)
            composer.merge(messages=item.messages)
            stream_response = streamer.chat_response(
                prompt=composer.merged_str,
                temperature=item.temperature,
                top_p=item.top_p,
                max_new_tokens=item.max_tokens,
                api_key=api_key,
                use_cache=item.use_cache,
            )

        if item.stream:
            event_source_response = EventSourceResponse(
                streamer.chat_return_generator(stream_response),
                media_type="text/event-stream",
                ping=2000,
                ping_message_factory=lambda: ServerSentEvent(**{"comment": ""}),
            )
            return event_source_response
        else:
            data_response = streamer.chat_return_dict(stream_response)
            return data_response

    def get_readme(self):
        readme_path = Path(__file__).parents[1] / "README.md"
        with open(readme_path, "r", encoding="utf-8") as rf:
            readme_str = rf.read()
        readme_html = markdown2.markdown(
            readme_str, extras=["table", "fenced-code-blocks", "highlightjs-lang"]
        )
        return readme_html

    def setup_routes(self):
        for prefix in ["", "/v1", "/api", "/api/v1"]:
            if prefix in ["/api/v1"]:
                include_in_schema = True
            else:
                include_in_schema = False

            self.app.get(
                prefix + "/models",
                summary="Get available models",
                include_in_schema=include_in_schema,
            )(self.get_available_models)

            self.app.post(
                prefix + "/chat/completions",
                summary="Chat completions in conversation session",
                include_in_schema=include_in_schema,
            )(self.chat_completions)
        self.app.get(
            "/readme",
            summary="README of HF LLM API",
            response_class=HTMLResponse,
            include_in_schema=False,
        )(self.get_readme)


class ArgParser(argparse.ArgumentParser):
    def __init__(self, *args, **kwargs):
        super(ArgParser, self).__init__(*args, **kwargs)

        self.add_argument(
            "-s",
            "--host",
            type=str,
            default=CONFIG["host"],
            help=f"Host for {CONFIG['app_name']}",
        )
        self.add_argument(
            "-p",
            "--port",
            type=int,
            default=CONFIG["port"],
            help=f"Port for {CONFIG['app_name']}",
        )

        self.add_argument(
            "-d",
            "--dev",
            default=False,
            action="store_true",
            help="Run in dev mode",
        )

        self.args = self.parse_args(sys.argv[1:])


app = ChatAPIApp().app

if __name__ == "__main__":
    args = ArgParser().args
    if args.dev:
        uvicorn.run("__main__:app", host=args.host, port=args.port, reload=True)
    else:
        uvicorn.run("__main__:app", host=args.host, port=args.port, reload=False)

    # python -m apis.chat_api      # [Docker] on product mode
    # python -m apis.chat_api -d   # [Dev]    on develop mode