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linedance-app/venv/lib/python3.12/site-packages/numba/tests/test_iteration.py

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+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()