import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, HfArgumentParser, TrainingArguments, pipeline, logging, TextStreamer
from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training, get_peft_model
import os, torch, wandb, platform, warnings
from datasets import load_dataset
from trl import SFTTrainer

hf_token = '..........'

tokenizer = AutoTokenizer.from_pretrained('./vistral-tokenizer')
bnb_config = BitsAndBytesConfig(
  load_in_4bit=True,
  bnb_4bit_quant_type="nf4",
  bnb_4bit_compute_dtype=torch.bfloat16,
  bnb_4bit_use_double_quant=True,
)
model = AutoModelForCausalLM.from_pretrained(
  'Viet-Mistral/Vistral-7B-Chat',
  device_map="auto",
  token=hf_token,
  quantization_config=bnb_config,
)
ft_model = PeftModel.from_pretrained(model, CHECKPOINT_PATH)

#torch.backends.cuda.enable_mem_efficient_sdp(False)
#torch.backends.cuda.enable_flash_sdp(False)

system_prompt = "Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực. Hãy luôn trả lời một cách hữu ích nhất có thể, đồng thời giữ an toàn."

stop_tokens = [tokenizer.eos_token_id, tokenizer('<|im_end|>')['input_ids'].pop()]

def chat_test():
  conversation = [{"role": "system", "content": system_prompt }]
  while True:
    human = input("Human: ")
    if human.lower() == "reset":
      conversation = [{"role": "system", "content": system_prompt }]
      print("The chat history has been cleared!")
      continue

    if human.lower() == "exit":
      break

    conversation.append({"role": "user", "content": human })
    formatted = tokenizer.apply_chat_template(conversation, tokenize=False) + "<|im_start|>assistant"
    tok = tokenizer(formatted, return_tensors="pt").to(ft_model.device)
    input_ids = tok['input_ids']

    out_ids = ft_model.generate(
      input_ids=input_ids,
      attention_mask=tok['attention_mask'],
      eos_token_id=stop_tokens,
      max_new_tokens=50,
      do_sample=True,
      top_p=0.95,
      top_k=40,
      temperature=0.1,
      repetition_penalty=1.05,
    )
    assistant = tokenizer.batch_decode(out_ids[:, input_ids.size(1): ], skip_special_tokens=True)[0].strip()
    print("Assistant: ", assistant)
    conversation.append({"role": "assistant", "content": assistant })

chat_test()