import argparse
import random
from statistics import mean, stdev
from typing import List
import torch
import torchmetrics
from datasets import load_dataset
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModelForCausalLM


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        default=".",
        help="Path to the pre-trained model",
    )
    args = parser.parse_args()

    return args


def load_model(model_path: str):
    # Load the pre-trained model and tokenizer
    tokenizer = AutoTokenizer.from_pretrained(model_path, torch_dtype=torch.bfloat16)
    model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16).cuda().eval()

    eos_token_ids = [
        token_id
        for token_id in range(tokenizer.vocab_size)
        if "\n" in tokenizer.decode([token_id])
    ]

    if hasattr(model.config, "n_positions"):
        max_length = model.config.n_positions
    elif hasattr(model.config, "max_position_embeddings"):
        max_length = model.config.max_position_embeddings
    elif hasattr(model.config, "max_length"):
        max_length = model.config.max_length
    elif hasattr(model.config, "n_ctx"):
        max_length = model.config.n_ctx
    else:
        max_length = 32768  # Default value

    return {
        "name": model_path.split("/")[-1],
        "tokenizer": tokenizer,
        "model": model,
        "eos_token_ids": eos_token_ids,
        "max_length": max_length,
    }


def main():
    args = parse_args()

    model = load_model(args.model_name_or_path)
    
    model["model"].save_pretrained(
        args.model_name_or_path,
        max_shard_size="5GB"
    )


if __name__ == "__main__":
    main()