#    Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
#    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
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from e2enet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2


class nnUNetTrainerV2_noMirroring(nnUNetTrainerV2):
    def validate(self, do_mirroring: bool = True, use_sliding_window: bool = True,
                 step_size: float = 0.5, save_softmax: bool = True, use_gaussian: bool = True, overwrite: bool = True,
                 validation_folder_name: str = 'validation_raw', debug: bool = False, all_in_gpu: bool = False,
                 segmentation_export_kwargs: dict = None, run_postprocessing_on_folds: bool = True):
        """
        We need to wrap this because we need to enforce self.network.do_ds = False for prediction
        """
        ds = self.network.do_ds
        if do_mirroring:
            print("WARNING! do_mirroring was True but we cannot do that because we trained without mirroring. "
                  "do_mirroring was set to False")
        do_mirroring = False
        self.network.do_ds = False
        ret = super().validate(do_mirroring=do_mirroring, use_sliding_window=use_sliding_window, step_size=step_size,
                               save_softmax=save_softmax, use_gaussian=use_gaussian,
                               overwrite=overwrite, validation_folder_name=validation_folder_name, debug=debug,
                               all_in_gpu=all_in_gpu, segmentation_export_kwargs=segmentation_export_kwargs,
                               run_postprocessing_on_folds=run_postprocessing_on_folds)
        self.network.do_ds = ds
        return ret

    def setup_DA_params(self):
        super().setup_DA_params()
        self.data_aug_params["do_mirror"] = False
