import os
from threading import Thread
from typing import Iterator

import torch
from peft import PeftModel, PeftConfig
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from huggingface_hub import login

# model_id = 'meta-llama/Llama-2-13b-chat-hf'
#model_id = 'meta-llama/Llama-2-7b-chat-hf'
#model_id = 'Trelis/Llama-2-7b-chat-hf-sharded-bf16'
model_id = 'jaymojnidar/llama2-finetuned-mydata'
config_model_id = 'jaymojnidar/llama2-finetuned-mydata/adapter_config.json'
model_type = 'PEFT'


if torch.cuda.is_available():
    tok = os.environ['HF_TOKEN']
    login(new_session=True,
          write_permission=False,
          token=tok
          
          #, token="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"
          )
    
    if model_type == 'PEFT':
        config = PeftConfig.from_pretrained("jaymojnidar/llama2-finetuned-mydata")
        model = AutoModelForCausalLM.from_pretrained("Trelis/Llama-2-7b-chat-hf-sharded-bf16")
        model = PeftModel.from_pretrained(model, "jaymojnidar/llama2-finetuned-mydata")
    else:
        config = AutoConfig.from_pretrained(model_id, use_auth_token=True)
        config.pretraining_tp = 1
        model = AutoModelForCausalLM.from_pretrained(
            model_id,
            config=config,
            torch_dtype=torch.float16,
            #load_in_4bit=True,
            device_map='auto',
            use_auth_token=True
    )
    print("Loaded the model!")
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') #.to(torch.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:
        print(f"output text ->{text}<- end of text")
        outputs.append(text)
        yield ''.join(outputs)