ai-content-maker/.venv/Lib/site-packages/numba/tests/test_iteration.py

242 lines
6.9 KiB
Python

import unittest
import numpy as np
from numba import jit, njit
from numba.core import types, errors
from numba.tests.support import TestCase, MemoryLeakMixin
from numba.np import numpy_support
force_pyobj_flags = {'forceobj': True}
no_pyobj_flags = {'nopython': True}
def int_tuple_iter_usecase():
res = 0
for i in (1, 2, 99, 3):
res += i
return res
def float_tuple_iter_usecase():
res = 0.0
for i in (1.5, 2.0, 99.3, 3.4):
res += i
return res
def tuple_tuple_iter_usecase():
# Recursively homogeneous tuple type
res = 0.0
for i in ((1.5, 2.0), (99.3, 3.4), (1.8, 2.5)):
for j in i:
res += j
res = res * 2
return res
def enumerate_nested_tuple_usecase():
res = 0.0
for i, j in enumerate(((1.5, 2.0), (99.3, 3.4), (1.8, 2.5))):
for l in j:
res += i * l
res = res * 2
return res
def nested_enumerate_usecase():
res = 0.0
for i, (j, k) in enumerate(enumerate(((1.5, 2.0), (99.3, 3.4), (1.8, 2.5)))):
for l in k:
res += i * j * l
res = res * 2
return res
def enumerate_array_usecase():
res = 0
arrays = (np.ones(4), np.ones(5))
for i, v in enumerate(arrays):
res += v.sum()
return res
def scalar_iter_usecase(iterable):
res = 0.0
for x in iterable:
res += x
return res
def record_iter_usecase(iterable):
res = 0.0
for x in iterable:
res += x.a * x.b
return res
def record_iter_mutate_usecase(iterable):
for x in iterable:
x.a = x.a + x.b
record_dtype = np.dtype([('a', np.float64),
('b', np.int32),
])
class IterationTest(MemoryLeakMixin, TestCase):
def run_nullary_func(self, pyfunc, flags):
cfunc = jit((), **flags)(pyfunc)
expected = pyfunc()
self.assertPreciseEqual(cfunc(), expected)
def test_int_tuple_iter(self, flags=force_pyobj_flags):
self.run_nullary_func(int_tuple_iter_usecase, flags)
def test_int_tuple_iter_npm(self):
self.test_int_tuple_iter(flags=no_pyobj_flags)
# Type inference on tuples used to be hardcoded for ints, check
# that it works for other types.
def test_float_tuple_iter(self, flags=force_pyobj_flags):
self.run_nullary_func(float_tuple_iter_usecase, flags)
def test_float_tuple_iter_npm(self):
self.test_float_tuple_iter(flags=no_pyobj_flags)
def test_tuple_tuple_iter(self, flags=force_pyobj_flags):
self.run_nullary_func(tuple_tuple_iter_usecase, flags)
def test_tuple_tuple_iter_npm(self):
self.test_tuple_tuple_iter(flags=no_pyobj_flags)
def test_enumerate_nested_tuple(self, flags=force_pyobj_flags):
self.run_nullary_func(enumerate_nested_tuple_usecase, flags)
def test_enumerate_nested_tuple_npm(self):
self.test_enumerate_nested_tuple(flags=no_pyobj_flags)
def test_nested_enumerate(self, flags=force_pyobj_flags):
self.run_nullary_func(nested_enumerate_usecase, flags)
def test_nested_enumerate_npm(self):
self.test_nested_enumerate(flags=no_pyobj_flags)
def test_enumerate_refct(self):
# Test issue 3473
pyfunc = enumerate_array_usecase
cfunc = njit((),)(pyfunc)
expected = pyfunc()
self.assertPreciseEqual(cfunc(), expected)
def run_array_1d(self, item_type, arg, flags):
# Iteration over a 1d numpy array
pyfunc = scalar_iter_usecase
cfunc = jit(item_type(types.Array(item_type, 1, 'A'),), **flags)(pyfunc)
self.assertPreciseEqual(cfunc(arg), pyfunc(arg))
def test_array_1d_float(self, flags=force_pyobj_flags):
self.run_array_1d(types.float64, np.arange(5.0), flags)
def test_array_1d_float_npm(self):
self.test_array_1d_float(no_pyobj_flags)
def test_array_1d_complex(self, flags=force_pyobj_flags):
self.run_array_1d(types.complex128, np.arange(5.0) * 1.0j, flags)
def test_array_1d_complex_npm(self):
self.test_array_1d_complex(no_pyobj_flags)
def test_array_1d_record(self, flags=force_pyobj_flags):
pyfunc = record_iter_usecase
item_type = numpy_support.from_dtype(record_dtype)
cfunc = jit((types.Array(item_type, 1, 'A'),), **flags)(pyfunc)
arr = np.recarray(3, dtype=record_dtype)
for i in range(3):
arr[i].a = float(i * 2)
arr[i].b = i + 2
got = pyfunc(arr)
self.assertPreciseEqual(cfunc(arr), got)
def test_array_1d_record_npm(self):
self.test_array_1d_record(no_pyobj_flags)
def test_array_1d_record_mutate_npm(self, flags=no_pyobj_flags):
pyfunc = record_iter_mutate_usecase
item_type = numpy_support.from_dtype(record_dtype)
cfunc = jit((types.Array(item_type, 1, 'A'),), **flags)(pyfunc)
arr = np.recarray(3, dtype=record_dtype)
for i in range(3):
arr[i].a = float(i * 2)
arr[i].b = i + 2
expected = arr.copy()
pyfunc(expected)
got = arr.copy()
cfunc(got)
self.assertPreciseEqual(expected, got)
def test_array_1d_record_mutate(self):
self.test_array_1d_record_mutate_npm(flags=force_pyobj_flags)
def test_array_0d_raises(self):
def foo(x):
for i in x:
pass
# 0d is typing error
with self.assertRaises(errors.TypingError) as raises:
aryty = types.Array(types.int32, 0, 'C')
njit((aryty,))(foo)
self.assertIn("0-d array", str(raises.exception))
def test_tuple_iter_issue1504(self):
# The issue is due to `row` being typed as heterogeneous tuple.
def bar(x, y):
total = 0
for row in zip(x, y):
total += row[0] + row[1]
return total
x = y = np.arange(3, dtype=np.int32)
aryty = types.Array(types.int32, 1, 'C')
cfunc = njit((aryty, aryty))(bar)
expect = bar(x, y)
got = cfunc(x, y)
self.assertEqual(expect, got)
def test_tuple_of_arrays_iter(self):
# We used to leak a reference to each element of the tuple
def bar(arrs):
total = 0
for arr in arrs:
total += arr[0]
return total
x = y = np.arange(3, dtype=np.int32)
aryty = types.Array(types.int32, 1, 'C')
cfunc = njit((types.containers.UniTuple(aryty, 2),))(bar)
expect = bar((x, y))
got = cfunc((x, y))
self.assertEqual(expect, got)
class TestIterationRefct(MemoryLeakMixin, TestCase):
def test_zip_with_arrays(self):
@njit
def foo(sequence):
c = 0
for a, b in zip(range(len(sequence)), sequence):
c += (a + 1) * b.sum()
return
sequence = [np.arange(1 + i) for i in range(10)]
self.assertEqual(foo(sequence), foo.py_func(sequence))
if __name__ == '__main__':
unittest.main()