from abc import ABC
from collections import OrderedDict

import torch


class TestStepVariations(ABC):
    """
    Houses all variations of test steps
    """

    def test_step(self, batch, batch_idx, *args, **kwargs):
        """
        Default, baseline test_step
        :param batch:
        :return:
        """
        x, y = batch
        x = x.view(x.size(0), -1)
        y_hat = self(x)

        loss_test = self.loss(y, y_hat)

        # acc
        labels_hat = torch.argmax(y_hat, dim=1)
        test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
        test_acc = torch.tensor(test_acc)

        test_acc = test_acc.type_as(x)

        # alternate possible outputs to test
        if batch_idx % 1 == 0:
            output = OrderedDict({
                'test_loss': loss_test,
                'test_acc': test_acc,
            })
            return output
        if batch_idx % 2 == 0:
            return test_acc

        if batch_idx % 3 == 0:
            output = OrderedDict({
                'test_loss': loss_test,
                'test_acc': test_acc,
                'test_dic': {'test_loss_a': loss_test}
            })
            return output

    def test_step__multiple_dataloaders(self, batch, batch_idx, dataloader_idx, **kwargs):
        """
        Default, baseline test_step
        :param batch:
        :return:
        """
        x, y = batch
        x = x.view(x.size(0), -1)
        y_hat = self(x)

        loss_test = self.loss(y, y_hat)

        # acc
        labels_hat = torch.argmax(y_hat, dim=1)
        test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
        test_acc = torch.tensor(test_acc)

        test_acc = test_acc.type_as(x)

        # alternate possible outputs to test
        if batch_idx % 1 == 0:
            output = OrderedDict({
                'test_loss': loss_test,
                'test_acc': test_acc,
            })
            return output
        if batch_idx % 2 == 0:
            return test_acc

        if batch_idx % 3 == 0:
            output = OrderedDict({
                'test_loss': loss_test,
                'test_acc': test_acc,
                'test_dic': {'test_loss_a': loss_test}
            })
            return output
        if batch_idx % 5 == 0:
            output = OrderedDict({
                f'test_loss_{dataloader_idx}': loss_test,
                f'test_acc_{dataloader_idx}': test_acc,
            })
            return output

    def test_step__empty(self, batch, batch_idx, *args, **kwargs):
        return {}