import pytest
from gatfilter import filter_augment
from unittest.mock import Mock

# Mock data
def mock_load_dataset():
    return pd.DataFrame({
        'text': ['example text one', 'example text two'],
        'label': ['0', '1']
    })

def mock_word_model():
    class MockModel:
        def get_words(self):
            return ['example', 'text', 'one', 'two']

        def get_word_vector(self, word):
            return [0.1, 0.2] if word in ['example', 'text'] else [0.3, 0.4]
    
    return MockModel()

@pytest.fixture
def setup_data():
    word_model = mock_word_model()
    data_path = 'dummy_path/base.csv'
    augmented_path = 'dummy_path/augmented.csv'
    output_file_name = 'dummy_path/output.csv'
    return word_model, data_path, augmented_path, output_file_name

# Actual test
def test_filter_augment_basic_functionality(setup_data):
    word_model, data_path, augmented_path, output_file_name = setup_data
    
    # Mocking file read/write
    with patch('pandas.read_csv', side_effect=mock_load_dataset):
        with patch('pandas.DataFrame.to_csv') as mock_to_csv:
            result = filter_augment(word_model, data_path, augmented_path, output_file_name, plot=False, save_fig=False)
    
    # Assertions to verify the functionality
    assert isinstance(result, dict), "The function should return a dictionary."
    assert '0' in result and '1' in result, "There should be keys in the result for each label."
    assert isinstance(result['0'], set), "The values in the result dictionary should be sets."
    assert all(isinstance(word, str) for word in result['0']), "All items in the result sets should be strings."
