# import json
# import os
# from datetime import datetime, timezone
#
# from ..display.formatting import styled_error, styled_message, styled_warning
# from ..envs import API, EVAL_REQUESTS_PATH, QUEUE_REPO, TOKEN
# from .check_validity import (
#     already_submitted_models,
#     check_model_card,
#     get_model_size,
#     is_model_on_hub,
# )
#
# REQUESTED_MODELS = None
# USERS_TO_SUBMISSION_DATES = None
#
#
# def add_new_eval(
#     model: str,
#     base_model: str,
#     revision: str,
#     precision: str,
#     weight_type: str,
#     model_type: str,
# ):
#     global REQUESTED_MODELS
#     global USERS_TO_SUBMISSION_DATES
#     if not REQUESTED_MODELS:
#         REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(EVAL_REQUESTS_PATH)
#
#     user_name = ""
#     model_path = model
#     if "/" in model:
#         user_name = model.split("/")[0]
#         model_path = model.split("/")[1]
#
#     precision = precision.split(" ")[0]
#     current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
#
#     if model_type is None or model_type == "":
#         return styled_error("Please select a model type.")
#
#     # Does the model actually exist?
#     if revision == "":
#         revision = "main"
#
#     # Is the model on the hub?
#     if weight_type in ["Delta", "Adapter"]:
#         base_model_on_hub, error, _ = is_model_on_hub(
#             model_name=base_model, revision=revision, token=TOKEN, test_tokenizer=True
#         )
#         if not base_model_on_hub:
#             return styled_error(f'Base model "{base_model}" {error}')
#
#     if not weight_type == "Adapter":
#         model_on_hub, error, _ = is_model_on_hub(model_name=model, revision=revision, token=TOKEN, test_tokenizer=True)
#         if not model_on_hub:
#             return styled_error(f'Model "{model}" {error}')
#
#     # Is the model info correctly filled?
#     try:
#         model_info = API.model_info(repo_id=model, revision=revision)
#     except Exception:
#         return styled_error("Could not get your model information. Please fill it up properly.")
#
#     model_size = get_model_size(model_info=model_info, precision=precision)
#
#     # Were the model card and license filled?
#     try:
#         license = model_info.cardData["license"]
#     except Exception:
#         return styled_error("Please select a license for your model")
#
#     modelcard_OK, error_msg = check_model_card(model)
#     if not modelcard_OK:
#         return styled_error(error_msg)
#
#     # Seems good, creating the eval
#     print("Adding new eval")
#
#     eval_entry = {
#         "model": model,
#         "base_model": base_model,
#         "revision": revision,
#         "precision": precision,
#         "weight_type": weight_type,
#         "status": "PENDING",
#         "submitted_time": current_time,
#         "model_type": model_type,
#         "likes": model_info.likes,
#         "params": model_size,
#         "license": license,
#         "private": False,
#     }
#
#     # Check for duplicate submission
#     if f"{model}_{revision}_{precision}" in REQUESTED_MODELS:
#         return styled_warning("This model has been already submitted.")
#
#     print("Creating eval file")
#     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))
#
#     print("Uploading eval file")
#     API.upload_file(
#         path_or_fileobj=out_path,
#         path_in_repo=out_path.split("eval-queue/")[1],
#         repo_id=QUEUE_REPO,
#         repo_type="dataset",
#         commit_message=f"Add {model} to eval queue",
#     )
#
#     # Remove the local file
#     os.remove(out_path)
#
#     return styled_message(
#         "Your request has been submitted to the evaluation queue!\nPlease wait for up to an hour for the model to show in the PENDING list."
#     )