# Copyright The PyTorch Lightning team.
#
# 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 abc import ABC

import torch

from pytorch_lightning.utilities import DistributedType


class TestEpochEndVariations(ABC):

    def test_epoch_end(self, outputs):
        """
        Called at the end of test epoch to aggregate outputs
        :param outputs: list of individual outputs of each validation step
        :return:
        """
        # if returned a scalar from test_step, outputs is a list of tensor scalars
        # we return just the average in this case (if we want)
        # return torch.stack(outputs).mean()
        test_loss_mean = 0
        test_acc_mean = 0
        for output in outputs:
            test_loss = self.get_output_metric(output, 'test_loss')

            # reduce manually when using dp
            if self.trainer._distrib_type == DistributedType.DP:
                test_loss = torch.mean(test_loss)
            test_loss_mean += test_loss

            # reduce manually when using dp
            test_acc = self.get_output_metric(output, 'test_acc')
            if self.trainer._distrib_type == DistributedType.DP:
                test_acc = torch.mean(test_acc)

            test_acc_mean += test_acc

        test_loss_mean /= len(outputs)
        test_acc_mean /= len(outputs)

        metrics_dict = {'test_loss': test_loss_mean, 'test_acc': test_acc_mean}
        result = {'progress_bar': metrics_dict, 'log': metrics_dict}
        return result

    def test_epoch_end__multiple_dataloaders(self, outputs):
        """
        Called at the end of test epoch to aggregate outputs
        :param outputs: list of individual outputs of each validation step
        :return:
        """
        # if returned a scalar from test_step, outputs is a list of tensor scalars
        # we return just the average in this case (if we want)
        # return torch.stack(outputs).mean()
        test_loss_mean = 0
        test_acc_mean = 0
        i = 0
        for dl_output in outputs:
            for output in dl_output:
                test_loss = output['test_loss']

                # reduce manually when using dp
                if self.trainer._distrib_type == DistributedType.DP:
                    test_loss = torch.mean(test_loss)
                test_loss_mean += test_loss

                # reduce manually when using dp
                test_acc = output['test_acc']
                if self.trainer._distrib_type == DistributedType.DP:
                    test_acc = torch.mean(test_acc)

                test_acc_mean += test_acc
                i += 1

        test_loss_mean /= i
        test_acc_mean /= i

        tqdm_dict = {'test_loss': test_loss_mean, 'test_acc': test_acc_mean}
        result = {'progress_bar': tqdm_dict}
        return result