import torch
from dataclasses import dataclass
from peft import LoraConfig, get_peft_model
from transformers import LlamaForCausalLM, LlamaTokenizer


@dataclass
class LoraArguments:
    lora_r: int = 8
    lora_alpha: int = 16
    lora_dropout: float = 0.05
    lora_target_modules = ["q_proj", "v_proj"]
    lora_weight_path: str = ""
    bias: str = "none"
    

if __name__ == "__main__":
    device = 0
    lora_args = LoraArguments
    base_model = "TheBloke/vicuna-13B-1.1-HF"

    tokenizer = LlamaTokenizer.from_pretrained(base_model)
    model = LlamaForCausalLM.from_pretrained(
        base_model, load_in_8bit=True,
        torch_dtype=torch.float16, device_map={"": device}
    )

    lora_config = LoraConfig(
        r=lora_args.lora_r, lora_alpha=lora_args.lora_alpha, lora_dropout=lora_args.lora_dropout,
        target_modules=lora_args.lora_target_modules, bias=lora_args.bias, task_type="CAUSAL_LM",
    )
    model = get_peft_model(model, lora_config)

    weight = torch.load("pytorch_model.bin", map_location="cpu")
    model.load_state_dict(weight)

    prompt = (
        "A chat between a curious user and an artificial intelligence assistant. "
        "The assistant gives helpful, detailed, and polite answers to the user's questions. "
        "USER: You are tasked to demonstrate your writing skills in professional or work settings for the following question.\n"
        "Can you help me write a speech for a graduation ceremony, inspiring and motivating the graduates to pursue their dreams and make a positive impact on the world?\n"
        "Output: ASSISTANT: "
    )

    inputs = tokenizer([prompt], return_tensors="pt")
    inputs = {k: v.to("cuda:{}".format(device)) for k, v in inputs.items()}

    out = model.generate(
        **inputs, max_new_tokens=500, min_new_tokens=100, early_stopping=True, do_sample=True, top_k=8, temperature=0.75
    )
    decoded = tokenizer.decode(out[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
    print (decoded)