# MIT License
#
# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
# persons to whom the Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
# Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""
This module implements mixin abstract base class for all object detectors in ART.
"""

from abc import ABC, abstractmethod

from art.estimators.estimator import BaseEstimator
from art.estimators.classification.classifier import LossGradientsMixin


class ObjectDetectorMixin(ABC):
    """
    Mix-in Base class for ART object detectors.
    """

    @property
    @abstractmethod
    def native_label_is_pytorch_format(self) -> bool:
        """
        Are the native labels in PyTorch format [x1, y1, x2, y2]?
        """
        raise NotImplementedError


class ObjectDetector(ObjectDetectorMixin, LossGradientsMixin, BaseEstimator, ABC):
    """
    Typing variable definition.
    """

    pass
