# This Software (Dioptra) is being made available as a public service by the
# National Institute of Standards and Technology (NIST), an Agency of the United
# States Department of Commerce. This software was developed in part by employees of
# NIST and in part by NIST contractors. Copyright in portions of this software that
# were developed by NIST contractors has been licensed or assigned to NIST. Pursuant
# to Title 17 United States Code Section 105, works of NIST employees are not
# subject to copyright protection in the United States. However, NIST may hold
# international copyright in software created by its employees and domestic
# copyright (or licensing rights) in portions of software that were assigned or
# licensed to NIST. To the extent that NIST holds copyright in this software, it is
# being made available under the Creative Commons Attribution 4.0 International
# license (CC BY 4.0). The disclaimers of the CC BY 4.0 license apply to all parts
# of the software developed or licensed by NIST.
#
# ACCESS THE FULL CC BY 4.0 LICENSE HERE:
# https://creativecommons.org/licenses/by/4.0/legalcode
import mlflow
from mlflow.entities import Run as MlflowRun
from prefect import task
from tensorflow.keras.models import Model

from mitre.securingai import pyplugs
from mitre.securingai.sdk.exceptions import (
    ARTDependencyError,
    TensorflowDependencyError,
)
from mitre.securingai.sdk.utilities.decorators import require_package


@task
@require_package("art", exc_type=ARTDependencyError)
@require_package("tensorflow", exc_type=TensorflowDependencyError)
def add_model_manually_to_registry(
    model: Model, active_run: MlflowRun, artifact_path: str, model_name: str
) -> None:
    mlflow.keras.log_model(
        keras_model=model, artifact_path=artifact_path, registered_model_name=model_name
    )
