import json
import numpy as np
import os
from peft import PeftModel
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import shutil
os.environ["PEFT_BOFT_FORCE_CPU"] = "1"

def merge(base_model_path, peft_model_path, save_path):
    base_model = AutoModelForCausalLM.from_pretrained(base_model_path, torch_dtype="bfloat16")
    ft_model = PeftModel.from_pretrained(base_model, peft_model_path)
    ft_model = ft_model.merge_and_unload()
    ft_model.save_pretrained(save_path)

def copy_selected_items(src_path, dst_path, items):
    os.makedirs(dst_path, exist_ok=True)

    for item in items:
        source_item = os.path.join(src_path, item)
        dest_item = os.path.join(dst_path, item)

        if not os.path.exists(source_item):
            print(f"⚠ {item} در مسیر مبدا پیدا نشد!")
            continue

        if os.path.isdir(source_item):
            shutil.copytree(source_item, dest_item, dirs_exist_ok=True)
        elif os.path.isfile(source_item):
            shutil.copy2(source_item, dest_item)

def main():
    file_path = os.path.dirname(__file__)

    base_model_path = file_path + "/../../data/models/Qwen3-Embedding-0.6B/model"
    peft_model_path = file_path + "/output/v17-20251202-223944/checkpoint-387"
    save_path = file_path + "/output/v17-20251202-223944/merged_checkpoint-387"
    merge(base_model_path, peft_model_path, save_path)

    items = ["1_Pooling", "config_sentence_transformers.json", "merges.txt", "modules.json", "README.md", "tokenizer_config.json", "tokenizer.json", 
        "vocab.json"]
    copy_selected_items(base_model_path, save_path, items)


if __name__ == "__main__":
    main()