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# import json
# import os
# import pprint
# import re
# from datetime import datetime, timezone
# import click
# from colorama import Fore
# from huggingface_hub import HfApi, snapshot_download
# EVAL_REQUESTS_PATH = "eval-queue"
# QUEUE_REPO = "open-llm-leaderboard/requests"
# precisions = ("float16", "bfloat16", "8bit (LLM.int8)", "4bit (QLoRA / FP4)", "GPTQ")
# model_types = ("pretrained", "fine-tuned", "RL-tuned", "instruction-tuned")
# weight_types = ("Original", "Delta", "Adapter")
# def get_model_size(model_info, precision: str):
# size_pattern = size_pattern = re.compile(r"(\d\.)?\d+(b|m)")
# try:
# model_size = round(model_info.safetensors["total"] / 1e9, 3)
# except (AttributeError, TypeError):
# try:
# size_match = re.search(size_pattern, model_info.modelId.lower())
# model_size = size_match.group(0)
# model_size = round(float(model_size[:-1]) if model_size[-1] == "b" else float(model_size[:-1]) / 1e3, 3)
# except AttributeError:
# return 0 # Unknown model sizes are indicated as 0, see NUMERIC_INTERVALS in app.py
# size_factor = 8 if (precision == "GPTQ" or "gptq" in model_info.modelId.lower()) else 1
# model_size = size_factor * model_size
# return model_size
# def main():
# api = HfApi()
# current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
# snapshot_download(repo_id=QUEUE_REPO, revision="main", local_dir=EVAL_REQUESTS_PATH, repo_type="dataset")
# model_name = click.prompt("Enter model name")
# revision = click.prompt("Enter revision", default="main")
# precision = click.prompt("Enter precision", default="float16", type=click.Choice(precisions))
# model_type = click.prompt("Enter model type", type=click.Choice(model_types))
# weight_type = click.prompt("Enter weight type", default="Original", type=click.Choice(weight_types))
# base_model = click.prompt("Enter base model", default="")
# status = click.prompt("Enter status", default="FINISHED")
# try:
# model_info = api.model_info(repo_id=model_name, revision=revision)
# except Exception as e:
# print(f"{Fore.RED}Could not find model info for {model_name} on the Hub\n{e}{Fore.RESET}")
# return 1
# model_size = get_model_size(model_info=model_info, precision=precision)
# try:
# license = model_info.cardData["license"]
# except Exception:
# license = "?"
# eval_entry = {
# "model": model_name,
# "base_model": base_model,
# "revision": revision,
# "private": False,
# "precision": precision,
# "weight_type": weight_type,
# "status": status,
# "submitted_time": current_time,
# "model_type": model_type,
# "likes": model_info.likes,
# "params": model_size,
# "license": license,
# }
# user_name = ""
# model_path = model_name
# if "/" in model_name:
# user_name = model_name.split("/")[0]
# model_path = model_name.split("/")[1]
# pprint.pprint(eval_entry)
# if click.confirm("Do you want to continue? This request file will be pushed to the hub"):
# click.echo("continuing...")
# out_dir = f"{EVAL_REQUESTS_PATH}/{user_name}"
# os.makedirs(out_dir, exist_ok=True)
# out_path = f"{out_dir}/{model_path}_eval_request_{False}_{precision}_{weight_type}.json"
# with open(out_path, "w") as f:
# f.write(json.dumps(eval_entry))
# api.upload_file(
# path_or_fileobj=out_path,
# path_in_repo=out_path.split(f"{EVAL_REQUESTS_PATH}/")[1],
# repo_id=QUEUE_REPO,
# repo_type="dataset",
# commit_message=f"Add {model_name} to eval queue",
# )
# else:
# click.echo("aborting...")
# if __name__ == "__main__":
# main()
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