from threading import Thread
from typing import Iterator

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from transformers.generation.utils import GenerationConfig

model_id = 'baichuan-inc/Baichuan2-13B-Chat'

if torch.cuda.is_available():
    model = AutoModelForCausalLM.from_pretrained(
        model_id,
        device_map='auto',
        torch_dtype=torch.bfloat16,
        trust_remote_code=True
    )
    model.generation_config = GenerationConfig.from_pretrained(model_id)
else:
    model = None
tokenizer = AutoTokenizer.from_pretrained(
    model_id,
    use_fast=False,
    trust_remote_code=True
)

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 = 2048,
        temperature: float = 0.3,
        top_p: float = 0.85,
        top_k: int = 5) -> 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.,
      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,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

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