"""Tests for the testing infrastructure."""12import pytest345@pytest.mark.xfail6def test_memory_usage() -> None:7pytest.fail(reason="Disabled for now")8# """The ``memory_usage`` fixture gives somewhat accurate results."""9# memory_usage = memory_usage_without_pyarrow10# assert memory_usage.get_current() < 100_00011# assert memory_usage.get_peak() < 100_00012#13# # Memory from Python is tracked:14# b = b"X" * 1_300_00015# assert 1_300_000 <= memory_usage.get_current() <= 2_000_00016# assert 1_300_000 <= memory_usage.get_peak() <= 2_000_00017# del b18# assert memory_usage.get_current() <= 500_00019# assert 1_300_000 <= memory_usage.get_peak() <= 2_000_00020# memory_usage.reset_tracking()21# assert memory_usage.get_current() < 100_00022# assert memory_usage.get_peak() < 100_00023#24# # Memory from Polars is tracked:25# df = pl.DataFrame({"x": pl.arange(0, 1_000_000, eager=True, dtype=pl.Int64)})26# del df27# peak_bytes = memory_usage.get_peak()28# assert 8_000_000 <= peak_bytes < 8_500_00029#30# memory_usage.reset_tracking()31# assert memory_usage.get_peak() < 1_000_00032#33# # Memory from NumPy is tracked:34# arr = np.ones((1_400_000,), dtype=np.uint8)35# del arr36# peak = memory_usage.get_peak()37# assert 1_400_000 < peak < 1_500_000383940