import torch
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    TextIteratorStreamer,
    BitsAndBytesConfig,
)
from threading import Thread

import gradio as gr

try:
    import spaces
except:

    class spaces:
        @staticmethod
        def GPU(duration: int):
            return lambda x: x


MODEL_NAME = "hatakeyama-llm-team/Tanuki-8B-Instruct"

quantization_config = BitsAndBytesConfig(load_in_8bit=True)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME, quantization_config=quantization_config, device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)

print(model.hf_device_map)


@spaces.GPU(duration=10)
def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
    top_k,
):
    messages = [{"role": "system", "content": system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": message})

    tokenized_input = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, tokenize=True, return_tensors="pt"
    ).to(model.device)

    streamer = TextIteratorStreamer(
        tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True
    )
    generate_kwargs = dict(
        input_ids=tokenized_input,
        streamer=streamer,
        max_new_tokens=max_tokens,
        do_sample=True,
        temperature=temperature,
        top_k=top_k,
        top_p=top_p,
        num_beams=1,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    partial_message = ""
    for new_token in streamer:
        partial_message += new_token
        yield partial_message


demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(
            value="以下は、タスクを説明する指示と、文脈のある入力の組み合わせです。要求を適切に満たす応答を書きなさい。",
            label="システムプロンプト",
        ),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p",
        ),
        gr.Slider(minimum=1, maximum=2000, value=200, step=10, label="Top-k"),
    ],
    examples=[
        ["たぬきってなんですか？"],
        ["情けは人の為ならずとはどういう意味ですか？"],
        ["明晰夢とはなんですか？"],
        ["シュレディンガー方程式とシュレディンガーの猫はどのような関係がありますか？"],
    ],
)


if __name__ == "__main__":
    demo.launch()