from unsloth import FastLanguageModel
from peft import PeftModel
import torch
import json
from tqdm import tqdm
import re

# Base model id and LoRA adapter ID
base_model_id = "llm-jp/llm-jp-3-13b"
adapter_id = "Rumi/llm-jp_SFT_rn_2024-12-14_06"

# Log in with your Hugging Face token
HF_TOKEN = "hogehoge"
from huggingface_hub import login
login(HF_TOKEN)

# Download the original model
dtype = None
load_in_4bit = True

base_model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=base_model_id,
    dtype=dtype,
    load_in_4bit=load_in_4bit,
    trust_remote_code=True,
)

# Merge adapter to the base model
model = PeftModel.from_pretrained(base_model, adapter_id, token = HF_TOKEN)

# Read evaluation dataset
datasets = []
with open("./elyza-tasks-100-TV_0.jsonl", "r") as f:
    item = ""
    for line in f:
      line = line.strip()
      item += line
      if item.endswith("}"):
        datasets.append(json.loads(item))
        item = ""

# Change the format and conduct the evaluation
FastLanguageModel.for_inference(model)

results = []
for dt in tqdm(datasets):
  input = dt["input"]

  prompt = f"""### 指示\n{input}\n### 回答\n"""

  inputs = tokenizer([prompt], return_tensors = "pt").to(model.device)

  outputs = model.generate(**inputs, max_new_tokens = 512, use_cache = True, do_sample=False, repetition_penalty=1.2)
  prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).split('\n### 回答')[-1]

  results.append({"task_id": dt["task_id"], "input": input, "output": prediction})

# Save result in the jsonl format
json_file_id = re.sub(".*/", "", adapter_id)
with open(f"/content/{json_file_id}_output.jsonl", 'w', encoding='utf-8') as f:
    for result in results:
        json.dump(result, f, ensure_ascii=False)
        f.write('\n')