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�support�_multiprocessingN)�make_pkg�make_script�make_zip_pkg�make_zip_script�assert_python_okztest is not helpful for PGOa�# multiprocessing includes all sorts of shenanigans to make __main__
# attributes accessible in the subprocess in a pickle compatible way.

# We run the "doesn't work in the interactive interpreter" example from
# the docs to make sure it *does* work from an executed __main__,
# regardless of the invocation mechanism

import sys
import time
from multiprocessing import Pool, set_start_method

# We use this __main__ defined function in the map call below in order to
# check that multiprocessing in correctly running the unguarded
# code in child processes and then making it available as __main__
def f(x):
    return x*x

# Check explicit relative imports
if "check_sibling" in __file__:
    # We're inside a package and not in a __main__.py file
    # so make sure explicit relative imports work correctly
    from . import sibling

if __name__ == '__main__':
    start_method = sys.argv[1]
    set_start_method(start_method)
    results = []
    with Pool(5) as pool:
        pool.map_async(f, [1, 2, 3], callback=results.extend)
        start_time = time.monotonic()
        while not results:
            time.sleep(0.05)
            # up to 1 min to report the results
            dt = time.monotonic() - start_time
            if dt > 60.0:
                raise RuntimeError("Timed out waiting for results (%.1f sec)" % dt)

    results.sort()
    print(start_method, "->", results)

    pool.join()
a�# __main__.py files have an implied "if __name__ == '__main__'" so
# multiprocessing should always skip running them in child processes

# This means we can't use __main__ defined functions in child processes,
# so we just use "int" as a passthrough operation below

if __name__ != "__main__":
    raise RuntimeError("Should only be called as __main__!")

import sys
import time
from multiprocessing import Pool, set_start_method

start_method = sys.argv[1]
set_start_method(start_method)
results = []
with Pool(5) as pool:
    pool.map_async(int, [1, 4, 9], callback=results.extend)
    start_time = time.monotonic()
    while not results:
        time.sleep(0.05)
        # up to 1 min to report the results
        dt = time.monotonic() - start_time
        if dt > 60.0:
            raise RuntimeError("Timed out waiting for results (%.1f sec)" % dt)

results.sort()
print(start_method, "->", results)

pool.join()
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