text: # If data is not present then skip
  import os
  
  import pytest
  import torch
  from omegaconf import OmegaConf
  
  # import src.data.make_dataset
  from src.data.get_dataset import main
  
  
  # import _src.__main__
  @pytest.mark.skipif(
      not os.path.exists("data/processed/"), reason="Train files not found"
  )
  @pytest.mark.skipif(
      not os.path.exists("data/processed/"), reason="Test files not found"
  )
  
  # See how much of data is run based on tests
  # RUN test : coverage run -m pytest tests/
  # See coverage : coverage report
  
  
  class TestClass:
      """Tests on get_dataset"""
  
      def test_InputPath_Exists(self):
          """
          Test value error raised if input_path does not exist
          """
          CONFIG = OmegaConf.create(
              {
                  "hyperparameters": {
                      "TRAIN_BATCHSIZE": 4,
                      "TEST_SIZE": 0.2,
                  },
                  "paths": {
                      "input_filepath": os.getcwd() + "/test/some/path",
                  },
              }
          )
          with pytest.raises(ValueError, match="Input path does not exist"):
              main(CONFIG)
  
      def test_outputImgSize(self):
          """
          Test if images from get_dataset are 64x64
          """
          CONFIG = OmegaConf.create(
              {
                  "hyperparameters": {
                      "TRAIN_BATCHSIZE": 64,
                      "TEST_SIZE": 0.2,
                  },
                  "paths": {
                      "input_filepath": os.getcwd() + "/data/processed/",
                  },
              }
          )
          train_loader, _, test_loader = main(CONFIG)
          for images, _ in train_loader:
              assert images.shape[2:4] == torch.Size([64, 64])
  
          for images, _ in test_loader:
              assert images.shape[2:4] == torch.Size([64, 64])
  