242 lines
7.0 KiB
Python
242 lines
7.0 KiB
Python
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import numpy as np
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from numba import jit, njit
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from numba.core import types
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from numba.tests.support import TestCase, tag
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import unittest
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def dobool(a):
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return bool(a)
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def doint(a):
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return int(a)
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def dofloat(a):
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return float(a)
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def docomplex(a):
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return complex(a)
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def docomplex2(a, b):
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return complex(a, b)
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def complex_calc(a):
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z = complex(a)
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return z.real ** 2 + z.imag ** 2
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def complex_calc2(a, b):
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z = complex(a, b)
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return z.real ** 2 + z.imag ** 2
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def converter(tp):
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def f(a):
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return tp(a)
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return f
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def real_np_types():
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for tp_name in ('int8', 'int16', 'int32', 'int64',
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'uint8', 'uint16', 'uint32', 'uint64',
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'intc', 'uintc', 'intp', 'uintp',
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'float32', 'float64', 'bool_'):
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yield tp_name
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def complex_np_types():
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for tp_name in ('complex64', 'complex128'):
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yield tp_name
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class TestScalarNumberCtor(TestCase):
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"""
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Test <number class>(some scalar)
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"""
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def check_int_constructor(self, pyfunc):
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x_types = [
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types.boolean, types.int32, types.int64, types.float32, types.float64
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]
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x_values = [1, 0, 1000, 12.2, 23.4]
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for ty, x in zip(x_types, x_values):
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cfunc = njit((ty,))(pyfunc)
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self.assertPreciseEqual(pyfunc(x), cfunc(x))
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def test_bool(self):
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self.check_int_constructor(dobool)
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def test_int(self):
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self.check_int_constructor(doint)
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def test_float(self):
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pyfunc = dofloat
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x_types = [
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types.int32, types.int64, types.float32, types.float64
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]
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x_values = [1, 1000, 12.2, 23.4]
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for ty, x in zip(x_types, x_values):
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cfunc = njit((ty,))(pyfunc)
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self.assertPreciseEqual(pyfunc(x), cfunc(x),
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prec='single' if ty is types.float32 else 'exact')
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def test_complex(self):
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pyfunc = docomplex
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x_types = [
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types.int32, types.int64, types.float32, types.float64,
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types.complex64, types.complex128,
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]
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x_values = [1, 1000, 12.2, 23.4, 1.5-5j, 1-4.75j]
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for ty, x in zip(x_types, x_values):
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cfunc = njit((ty,))(pyfunc)
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got = cfunc(x)
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expected = pyfunc(x)
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self.assertPreciseEqual(pyfunc(x), cfunc(x),
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prec='single' if ty is types.float32 else 'exact')
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# Check that complex(float32) really creates a complex64,
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# by checking the accuracy of computations.
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pyfunc = complex_calc
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x = 1.0 + 2**-50
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cfunc = njit((types.float32,))(pyfunc)
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self.assertPreciseEqual(cfunc(x), 1.0)
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# Control (complex128)
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cfunc = njit((types.float64,))(pyfunc)
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self.assertGreater(cfunc(x), 1.0)
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def test_complex2(self):
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pyfunc = docomplex2
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x_types = [
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types.int32, types.int64, types.float32, types.float64
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]
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x_values = [1, 1000, 12.2, 23.4]
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y_values = [x - 3 for x in x_values]
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for ty, x, y in zip(x_types, x_values, y_values):
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cfunc = njit((ty, ty))(pyfunc)
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self.assertPreciseEqual(pyfunc(x, y), cfunc(x, y),
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prec='single' if ty is types.float32 else 'exact')
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# Check that complex(float32, float32) really creates a complex64,
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# by checking the accuracy of computations.
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pyfunc = complex_calc2
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x = 1.0 + 2**-50
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cfunc = njit((types.float32, types.float32))(pyfunc)
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self.assertPreciseEqual(cfunc(x, x), 2.0)
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# Control (complex128)
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cfunc = njit((types.float64, types.float32))(pyfunc)
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self.assertGreater(cfunc(x, x), 2.0)
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def check_type_converter(self, tp, np_type, values):
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pyfunc = converter(tp)
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cfunc = jit(nopython=True)(pyfunc)
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if issubclass(np_type, np.integer):
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# Converting from a Python int to a small Numpy int on 32-bit
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# builds can raise "OverflowError: Python int too large to
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# convert to C long". Work around by going through a large
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# Numpy int first.
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np_converter = lambda x: np_type(np.int64(x))
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else:
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np_converter = np_type
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dtype = np.dtype(np_type)
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for val in values:
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if dtype.kind == 'u' and isinstance(val, float) and val < 0.0:
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# Converting negative float to unsigned int yields undefined
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# behaviour (and concretely different on ARM vs. x86)
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continue
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expected = np_converter(val)
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got = cfunc(val)
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self.assertPreciseEqual(got, expected,
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msg="for type %s with arg %s" % (np_type, val))
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def check_number_types(self, tp_factory):
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values = [0, 1, -1, 100003, 10000000000007, -100003, -10000000000007,
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1.5, -3.5]
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for tp_name in real_np_types():
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np_type = getattr(np, tp_name)
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tp = tp_factory(tp_name)
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self.check_type_converter(tp, np_type, values)
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values.append(1.5+3j)
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for tp_name in complex_np_types():
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np_type = getattr(np, tp_name)
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tp = tp_factory(tp_name)
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self.check_type_converter(tp, np_type, values)
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def test_numba_types(self):
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"""
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Test explicit casting to Numba number types.
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"""
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def tp_factory(tp_name):
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return getattr(types, tp_name)
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self.check_number_types(tp_factory)
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def test_numpy_types(self):
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"""
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Test explicit casting to Numpy number types.
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"""
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def tp_factory(tp_name):
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return getattr(np, tp_name)
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self.check_number_types(tp_factory)
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class TestArrayNumberCtor(TestCase):
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"""
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Test <number class>(some sequence)
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"""
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def check_type_constructor(self, np_type, values):
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pyfunc = converter(np_type)
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cfunc = jit(nopython=True)(pyfunc)
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for val in values:
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expected = np_type(val)
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got = cfunc(val)
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self.assertPreciseEqual(got, expected)
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def test_1d(self):
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values = [
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(1.0, 2.5),
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(1, 2.5),
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[1.0, 2.5],
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(),
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]
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for tp_name in real_np_types():
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np_type = getattr(np, tp_name)
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self.check_type_constructor(np_type, values)
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values = [
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(1j, 2.5),
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[1.0, 2.5],
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]
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for tp_name in complex_np_types():
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np_type = getattr(np, tp_name)
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self.check_type_constructor(np_type, values)
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def test_2d(self):
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values = [
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((1.0, 2.5), (3.5, 4)),
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[(1.0, 2.5), (3.5, 4.0)],
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([1.0, 2.5], [3.5, 4.0]),
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[(), ()],
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]
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for tp_name in real_np_types():
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np_type = getattr(np, tp_name)
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self.check_type_constructor(np_type, values)
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for tp_name in complex_np_types():
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np_type = getattr(np, tp_name)
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self.check_type_constructor(np_type, values)
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if __name__ == '__main__':
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unittest.main()
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