import sys
from threading import Thread

import gradio as gr
import spaces
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    TextIteratorStreamer,
    BitsAndBytesConfig,
)


import torch


MODEL = "microsoft/Phi-3.5-mini-instruct"

if torch.cuda.is_available():
    device = "cuda"
elif sys.platform == "darwin" and torch.backends.mps.is_available():
    device = "mps"
else:
    device = "cpu"


# TODO understand this
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
)

tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForCausalLM.from_pretrained(
    MODEL,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=quantization_config,
)


@spaces.GPU()
def stream_chat(
    message: str,
    history: list,
    system_prompt: str,
    temperature: float = 0.8,
    max_new_tokens: int = 1024,
    top_p: float = 1.0,
    top_k: int = 20,
    penalty: float = 1.2,
):
    print(f"message: {message}")
    print(f"history: {history}")

    conversation = [{"role": "system", "content": system_prompt}]
    for prompt, answer in history:
        conversation.extend(
            [
                {"role": "user", "content": prompt},
                {"role": "assistant", "content": answer},
            ]
        )

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

    input_ids = tokenizer.apply_chat_template(
        conversation, add_generation_prompt=True, return_tensors="pt"
    ).to(model.device)

    streamer = TextIteratorStreamer(
        tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True
    )

    generate_kwargs = dict(
        input_ids=input_ids,
        max_new_tokens=max_new_tokens,
        do_sample=False if temperature == 0 else True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        eos_token_id=[128001, 128008, 128009],
        streamer=streamer,
    )

    with torch.no_grad():
        thread = Thread(target=model.generate, kwargs=generate_kwargs)
        thread.start()

    buffer = ""
    for new_text in streamer:
        buffer += new_text
        yield buffer


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    stream_chat,
    additional_inputs=[
        gr.Textbox(value="You are an ARM Assembly language decoder. You receive a line of Arm assembly and respond with a description of what the instruction does.", label="System message"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)


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