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| # Required Libraries | |
| import unittest | |
| import torch | |
| from models.helpers import positional_encoding | |
| class TestPositionalEncoding(unittest.TestCase): | |
| def test_positional_encoding(self): | |
| # Test with d_model=128, length=10 | |
| d_model = 128 | |
| length = 10 | |
| result = positional_encoding(d_model, length) | |
| # Assert that output is a torch.Tensor | |
| self.assertIsInstance(result, torch.Tensor) | |
| # Assert the output tensor shape is correct | |
| # The extra dimension from unsqueeze is considered | |
| expected_shape = (1, length, d_model) | |
| self.assertEqual(result.shape, expected_shape) | |
| # Assert that values lie in the range [-1, 1] | |
| self.assertTrue(torch.all((result >= -1) & (result <= 1))) | |
| # Run tests | |
| if __name__ == "__main__": | |
| unittest.main() | |