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import importlib
utils = importlib.import_module('extensions.sd-webui-controlnet.tests.utils', 'utils')
utils.setup_test_env()
from scripts.utils import ndarray_lru_cache
import unittest
import numpy as np
class TestNumpyLruCache(unittest.TestCase):
def setUp(self):
self.arr1 = np.array([1, 2, 3, 4, 5])
self.arr2 = np.array([1, 2, 3, 4, 5])
@ndarray_lru_cache(max_size=128)
def add_one(self, arr):
return arr + 1
def test_same_array(self):
# Test that the decorator works with numpy arrays.
result1 = self.add_one(self.arr1)
result2 = self.add_one(self.arr1)
# If caching is working correctly, these should be the same object.
self.assertIs(result1, result2)
def test_different_array_same_data(self):
# Test that the decorator works with different numpy arrays with the same data.
result1 = self.add_one(self.arr1)
result2 = self.add_one(self.arr2)
# If caching is working correctly, these should be the same object.
self.assertIs(result1, result2)
def test_cache_size(self):
# Test that the cache size limit is respected.
arrs = [np.array([i]) for i in range(150)]
# Add all arrays to the cache.
result1 = self.add_one(arrs[0])
for arr in arrs[1:]:
self.add_one(arr)
# Check that the first array is no longer in the cache.
result2 = self.add_one(arrs[0])
# If the cache size limit is working correctly, these should not be the same object.
self.assertIsNot(result1, result2)
def test_large_array(self):
# Create two large arrays with the same elements in the beginning and end, but one different element in the middle.
arr1 = np.ones(10000)
arr2 = np.ones(10000)
arr2[len(arr2)//2] = 0
result1 = self.add_one(arr1)
result2 = self.add_one(arr2)
# If hashing is working correctly, these should not be the same object because the input arrays are not equal.
self.assertIsNot(result1, result2)
if __name__ == '__main__':
unittest.main() |