from threading import Thread
from typing import Iterator

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig

print(f"Is CUDA available: {torch.cuda.is_available()}")
print(f"{torch.cuda.current_device()}")
print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")

model_id = "TheBloke/Chronos-Beluga-v2-13B-GPTQ"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoGPTQForCausalLM.from_quantized(model_id,
        use_safetensors=True,
        trust_remote_code=False,
        device="cuda:0",
        use_triton=False,
        quantize_config=None)

# 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']
    texts = [f'{system_prompt}\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} {response.strip()} ')
    message = message.strip() if do_strip else message
    texts.append(f'{message}')
    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.,
                                    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)