# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#     http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import annotations

from monai.config import KeysCollection
from monai.transforms.traits import InvertibleTrait
from monai.transforms.transform import MapTransform

__all__ = ["ApplyPendingd", "ApplyPendingD", "ApplyPendingDict"]


class ApplyPendingd(InvertibleTrait, MapTransform):
    """
    ApplyPendingd can be inserted into a pipeline that is being executed lazily in order
    to ensure resampling happens before the next transform. It doesn't do anything itself,
    but its presence causes the pipeline to be executed as it doesn't implement ``LazyTrait``

    See ``Compose`` for a detailed explanation of the lazy resampling feature.

    Args:
        keys: the keys for tensors that should have their pending transforms executed
    """

    def __init__(self, keys: KeysCollection):
        super().__init__(keys)

    def __call__(self, data):
        return data

    def inverse(self, data):
        return data


ApplyPendingD = ApplyPendingDict = ApplyPendingd