File: //usr/local/lib/python3.10/test/__pycache__/test_multiprocessing_main_handling.cpython-310.pyc
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import_helper�_multiprocessingN)� os_helper)�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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