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import unittest
import numpy as np
import torch
from models.helpers.tools import initialize_embeddings
class TestInitializeEmbeddings(unittest.TestCase):
def test_initialize_embeddings(self):
# Test with correct input shape
shape = (5, 10)
result = initialize_embeddings(shape)
# Assert output is torch.Tensor
self.assertIsInstance(result, torch.Tensor)
# Assert output shape
self.assertEqual(result.shape, shape)
# Assert type of elements
self.assertEqual(result.dtype, torch.float32)
# Assert standard deviation is close to expected (within some tolerance)
expected_stddev = np.sqrt(2 / shape[1])
tolerance = 0.1
self.assertLessEqual(abs(result.std().item() - expected_stddev), tolerance)
# Test with incorrect number of dimensions in shape
incorrect_shape = (5, 10, 15)
with self.assertRaises(AssertionError) as context:
initialize_embeddings(incorrect_shape)
self.assertEqual(
str(context.exception), "Can only initialize 2-D embedding matrices ...",
)
# Test with zero dimensions in shape
zero_dim_shape = ()
with self.assertRaises(AssertionError) as context:
initialize_embeddings(zero_dim_shape)
self.assertEqual(
str(context.exception), "Can only initialize 2-D embedding matrices ...",
)
# Run tests
if __name__ == "__main__":
unittest.main()
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