from threading import Thread
from typing import Iterator

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer

model_id = "meta-llama/Llama-2-7b-chat-hf"

if torch.cuda.is_available():
    model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
else:
    model = None
tokenizer = AutoTokenizer.from_pretrained(model_id)


def get_prompt(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> str:
    texts = [f"<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n"]
    # The first user input is _not_ stripped
    do_strip = False
    for user_input, response in chat_history:
        user_input = user_input.strip() if do_strip else user_input
        do_strip = True
        texts.append(f"{user_input} [/INST] {response.strip()} </s><s>[INST] ")
    message = message.strip() if do_strip else message
    texts.append(f"{message} [/INST]")
    return "".join(texts)


def get_input_token_length(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> int:
    prompt = get_prompt(message, chat_history, system_prompt)
    input_ids = tokenizer([prompt], return_tensors="np", add_special_tokens=False)["input_ids"]
    return input_ids.shape[-1]


def run(
    message: str,
    chat_history: list[tuple[str, str]],
    system_prompt: str,
    max_new_tokens: int = 1024,
    temperature: float = 0.8,
    top_p: float = 0.95,
    top_k: int = 50,
) -> Iterator[str]:
    prompt = get_prompt(message, chat_history, system_prompt)
    inputs = tokenizer([prompt], return_tensors="pt", add_special_tokens=False).to("cuda")

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

    outputs = []
    for text in streamer:
        outputs.append(text)
        yield "".join(outputs)