import os
import torch
from threading import Thread
from typing import Iterator
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    TextIteratorStreamer,
    StoppingCriteria, 
    StoppingCriteriaList
)

from huggingface_hub import login
login(token=os.environ["hf_read_token"])


class StopWordsCriteria(StoppingCriteria):
    def __init__(self, tokenizer, stop_words, stop_ids, stream_callback):
        self._tokenizer = tokenizer
        self._stop_words = stop_words
        self._stop_ids = stop_ids
        self._partial_result = ''
        self._stream_buffer = ''
        self._stream_callback = stream_callback

    # use both stop words (human id) and stop token ids (EOS tokens)
    def __call__(
            self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
            ) -> bool:
        first = not self._partial_result
        text = self._tokenizer.decode(input_ids[0, -1])
        self._partial_result += text
        # Check stop words
        for stop_word in self._stop_words:
            if stop_word in self._partial_result:
                return True
        # Check stop ids
        for stop_id in self._stop_ids:
            if input_ids[0][-1] == stop_id:
                return True
        if self._stream_callback:
            if first:
                text = text.lstrip()
            # buffer tokens if the partial result ends with a prefix of a stop word, e.g. "<hu"
            for stop_word in self._stop_words:
                for i in range(1, len(stop_word)):
                    if self._partial_result.endswith(stop_word[0:i]):
                        self._stream_buffer += text
                        return False
            self._stream_callback(self._stream_buffer + text)
            self._stream_buffer = ''
        return False
        

model_id = "medalpaca/medalpaca-7b"

if torch.cuda.is_available():
    model = AutoModelForCausalLM.from_pretrained(
        model_id,
        torch_dtype=torch.float16,
        device_map='auto',
	use_auth_token=True,
    )
else:
    model = None

tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=True)


def get_prompt(message: str, chat_history: list[tuple[str, str]],
               system_prompt: str) -> str:
    texts = [f'<<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} <Answer>: {response.strip()} <Question>: ')
    message = message.strip() if do_strip else message
    texts.append(f'{message} <Answer>:')
    print(texts)
    print('---------------------------------------------')
    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_token_type_ids=False, 
        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 = 0.90,
        top_k: int = 20) -> Iterator[str]:
    prompt = get_prompt(message, chat_history, system_prompt)
    print(prompt)
    print('=================================================')
    inputs = tokenizer(
        [prompt], 
        return_token_type_ids=False, 
        return_tensors='pt', 
        add_special_tokens=False).to('cuda')

    streamer = TextIteratorStreamer(tokenizer,
                                    timeout=10.,
                                    skip_prompt=True,
                                    skip_special_tokens=True)
    stop_criteria = StopWordsCriteria(
        tokenizer=tokenizer, 
        stop_words=["<Question>", "<Answer>"], 
        stop_ids=[1,2,32001,32002],
        stream_callback=None
    )
        
    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,
        stopping_criteria=StoppingCriteriaList([stop_criteria]),
        num_beams=1,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

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