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# import json
# import os
# from datetime import datetime, timezone
#
# from src.display.formatting import styled_error, styled_message, styled_warning
# from src.envs import API, EVAL_REQUESTS_PATH, TOKEN, QUEUE_REPO
# from src.submission.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."
# )
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