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Pin the int64 dtype of the numpy array literal test case - #696
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Fixes the cp311 Windows wheel job of the Release workflow, which fails every run since #700 in `test_a_dictionary_is_decoded_once_per_large_batch` before any DuckDB code runs: pyarrow.lib.ArrowIndexError: Index -1074 out of bounds The test builds its dictionary indices as `np.arange(2_000_000) * 1_000_000 // 2_000_000`. On Windows with numpy below 2, `np.arange` defaults to int32 (the C `long`), so the product overflows and about half the indices come out negative. The cp311 job is the only Windows job that installs numpy below 2 (the tensorflow pin), which is why neither the PR workflow nor the other wheel jobs saw it. An int32 simulation gives the same minimum of -1074. The arange now carries `dtype=np.int64`. Same class as #696; the other `np.arange` calls in the tests are small, float, or typed.
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The
Wheel: cp311-win_amd64release job has failed since #686 landed:The cp311 test group pins
numpy<2for tensorflow, and numpy 1.x on Windows usesint32 as the default integer type (the C
long).np.array([5, -3])is therefore anint32 array there, and
littypes it asARRAY<INT>, which is what Spark does for anint32 array as well. The test expected
ARRAY<BIGINT>, assuming a 64 bit default.The case now spells
dtype=np.int64, so it checks the int64 mapping on every platform.The int32 mapping is already covered by the neighbouring
np.array([5], dtype=np.int32)case.