from threading import Thread
from typing import Iterator

#import torch
from transformers.utils import logging
from ctransformers import AutoModelForCausalLM 
from transformers import TextIteratorStreamer, AutoTokenizer

logging.set_verbosity_info()
logger = logging.get_logger("transformers")

config = {'max_new_tokens': 256, 'repetition_penalty': 1.1, 
          'temperature': 0.1, 'stream': True}
model_id = 'TheBloke/Llama-2-7B-Chat-GGML'
device = "cpu"


model = AutoModelForCausalLM.from_pretrained(model_id, model_type="llama", lib='avx2', hf=True)
tokenizer = AutoTokenizer.from_pretrained('meta-llama/Llama-2-7b-chat-hf')

def get_prompt(message: str, chat_history: list[tuple[str, str]],
               system_prompt: str) -> str:
    logger.info("get_prompt chat_history=%s",chat_history)
    logger.info("get_prompt system_prompt=%s",system_prompt)
    texts = [f'<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n']
    logger.info("texts=%s",texts)
    # 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
    logger.info("get_prompt message=%s",message)
    texts.append(f'{message} [/INST]')
    logger.info("get_prompt final texts=%s",texts)
    return ''.join(texts)


def get_input_token_length(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> int:
    logger.info("get_input_token_length=%s",message)
    prompt = get_prompt(message, chat_history, system_prompt)
    logger.info("prompt=%s",prompt)
    input_ids = tokenizer([prompt], return_tensors='np', add_special_tokens=False)['input_ids']
    logger.info("input_ids=%s",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(device)

    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)