import json
import gradio as gr
from utils.logging_util import logger
from models.cpp_qwen2 import bot
# from models.hf_qwen2 import bot


#
# def postprocess(self, y):
#     if y is None:
#         return []
#     for i, (message, response) in enumerate(y):
#         y[i] = (
#             None if message is None else mdtex2html.convert((message)),
#             None if response is None else mdtex2html.convert(response),
#         )
#     return y
#
# gr.Chatbot.postprocess = postprocess

system_list = [
    "You are a helpful assistant.",
    "你是一个导游。",
    "你是一名投资经理。",
    "你是一名医生。",
    "你是一个英语老师。",
    "你是一个程序员。",
    "你是一个心理咨询师。",
    "你是一名AI写作助手。"
    "你是一名作家，擅长写小说。"
]

bot.pre_cache_system(system_list)

def generate_user_message(chatbot, history):
    if history and history[-1]["role"] == "user":
        gr.Warning('You should generate assistant-response.')
        yield chatbot, history
    else:
        chatbot.append(None)
        streamer = bot.generate(history, stream=True)
        for user_content, user_tokens in streamer:
            chatbot[-1] = (user_content, None)
            yield chatbot, history
        user_tokens = bot.strip_stoptokens(user_tokens)
        history.append({"role": "user", "content": user_content, "tokens": user_tokens})
        yield chatbot, history


def generate_assistant_message(chatbot, history):
    """
    auto-mode：query is None
    manual-mode：query 是用户输入
    """
    logger.info(f"generating {json.dumps(history, ensure_ascii=False)}")
    user_content = history[-1]["content"]
    if history[-1]["role"] != "user":
        gr.Warning('You should generate or type user-input first.')
        yield chatbot, history
    else:
        streamer = bot.generate(history, stream=True)
        for assistant_content, assistant_tokens in streamer:
            chatbot[-1] = (user_content, assistant_content)
            yield chatbot, history

        assistant_tokens = bot.strip_stoptokens(assistant_tokens)
        history.append({"role": "assistant", "content": assistant_content, "tokens": assistant_tokens})
        print(f"chatbot is {chatbot}")
        print(f"history is {history}")
        yield chatbot, history


def generate(chatbot, history):
    logger.info(f"chatbot: {chatbot}; history: {history}")
    streamer = None
    if history[-1]["role"] in ["assistant", "system"]:
        streamer = generate_user_message(chatbot, history)
    elif history[-1]["role"] == "user":
        streamer = generate_assistant_message(chatbot, history)
    else:
        gr.Warning("bug")

    for out in streamer:
        yield out


def append_user(input_content, chatbot, history):
    if history[-1]["role"] == "user":
        gr.Warning('You should generate assistant-response.')
        return chatbot, history

    chatbot.append((input_content, None))
    history.append({"role": "user", "content": input_content})
    return chatbot, history


def append_assistant(input_content, chatbot, history):
    if history[-1]["role"] != "user":
        gr.Warning('You should generate or type user-input first.')
        return chatbot, history
    chatbot[-1] = (chatbot[-1][0], input_content)
    history.append({"role": "assistant", "content": input_content})
    return chatbot, history



def undo_generate(chatbot, history):
    if history[-1]["role"] == "user":
        history = history[:-1]
        chatbot = chatbot[:-1]
    elif history[-1]["role"] == "assistant":
        history = history[:-1]
        chatbot[-1] = (chatbot[-1][0], None)
    else:
        pass
    logger.info(f"after undo, {json.dumps(chatbot, ensure_ascii=False)}, {json.dumps(history, ensure_ascii=False)}")
    return chatbot, history


def reset_user_input():
    return gr.update(value='')


def reset_state(system):
    return [], [{"role": "system", "content": system}]


def set_max_new_tokens(max_new_tokens):
    bot.generation_kwargs["max_tokens"] = max_new_tokens

def set_temperature(temperature):
    bot.generation_kwargs["temperature"] = temperature
def set_top_p(top_p):
    bot.generation_kwargs["top_p"] = top_p

def set_top_k(top_k):
    bot.generation_kwargs["top_k"] = top_k