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

"""
This file will be copied to a temporary directory in order to
exercise caching compiled Numba functions.

See test_dispatcher.py.
"""

import sys

import numpy as np

from numba import jit, prange
from numba.core import types

from numba.tests.ctypes_usecases import c_sin
from numba.tests.support import TestCase, captured_stderr


@jit(cache=True, nopython=True)
def simple_usecase(x):
    return x

def simple_usecase_caller(x):
    return simple_usecase(x)


@jit(cache=True, nopython=True)
def add_usecase(x, y):
    return x + y + Z


@jit(cache=True, forceobj=True)
def add_objmode_usecase(x, y):
    object()
    return x + y + Z


@jit(nopython=True)
def add_nocache_usecase(x, y):
    return x + y + Z


@jit(cache=True, nopython=True)
def inner(x, y):
    return x + y + Z

@jit(cache=True, nopython=True)
def outer(x, y):
    return inner(-y, x)

@jit(cache=False, nopython=True)
def outer_uncached(x, y):
    return inner(-y, x)


@jit(cache=True, forceobj=True)
def looplifted(n):
    object()
    res = 0
    for i in range(n):
        res = res + i
    return res


@jit(cache=True, nopython=True)
def use_c_sin(x):
    return c_sin(x)

@jit(cache=True, nopython=True)
def use_c_sin_nest1(x):
    return use_c_sin(x)

@jit(cache=True, nopython=True)
def use_c_sin_nest2(x):
    return use_c_sin_nest1(x)


@jit(cache=True, nopython=True)
def ambiguous_function(x):
    return x + 2

renamed_function1 = ambiguous_function

@jit(cache=True, nopython=True)
def ambiguous_function(x):
    return x + 6

renamed_function2 = ambiguous_function


def make_closure(x):
    @jit(cache=True, nopython=True)
    def closure(y):
        return x + y

    return closure

closure1 = make_closure(3)
closure2 = make_closure(5)
closure3 = make_closure(7)
closure4 = make_closure(9)


biggie = np.arange(10**6)

@jit(cache=True, nopython=True)
def use_big_array():
    return biggie


Z = 1

# Exercise returning a record instance.  This used to hardcode the dtype
# pointer's value in the bitcode.

packed_record_type = np.dtype([('a', np.int8), ('b', np.float64)])
aligned_record_type = np.dtype([('a', np.int8), ('b', np.float64)], align=True)

packed_arr = np.empty(2, dtype=packed_record_type)
for i in range(packed_arr.size):
    packed_arr[i]['a'] = i + 1
    packed_arr[i]['b'] = i + 42.5

aligned_arr = np.array(packed_arr, dtype=aligned_record_type)

@jit(cache=True, nopython=True)
def record_return(ary, i):
    return ary[i]


class _TestModule(TestCase):
    """
    Tests for functionality of this module's functions.
    Note this does not define any "test_*" method, instead check_module()
    should be called by hand.
    """

    def check_module(self, mod):
        self.assertPreciseEqual(mod.add_usecase(2, 3), 6)
        self.assertPreciseEqual(mod.add_objmode_usecase(2, 3), 6)
        self.assertPreciseEqual(mod.outer_uncached(3, 2), 2)
        self.assertPreciseEqual(mod.outer(3, 2), 2)

        packed_rec = mod.record_return(mod.packed_arr, 1)
        self.assertPreciseEqual(tuple(packed_rec), (2, 43.5))
        aligned_rec = mod.record_return(mod.aligned_arr, 1)
        self.assertPreciseEqual(tuple(aligned_rec), (2, 43.5))


@jit(cache=True)
def first_class_function_mul(x):
    return x * x


@jit(cache=True)
def first_class_function_add(x):
    return x + x


@jit(cache=True)
def first_class_function_usecase(f, x):
    return f(x)


def self_test():
    mod = sys.modules[__name__]
    _TestModule().check_module(mod)


@jit(parallel=True, cache=True, nopython=True)
def parfor_usecase(ary):
    return ary * ary + ary