# 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()