晋太元中,武陵人捕鱼为业。缘溪行,忘路之远近。忽逢桃花林,夹岸数百步,中无杂树,芳草鲜美,落英缤纷。渔人甚异之,复前行,欲穷其林。 林尽水源,便得一山,山有小口,仿佛若有光。便舍船,从口入。初极狭,才通人。复行数十步,豁然开朗。土地平旷,屋舍俨然,有良田、美池、桑竹之属。阡陌交通,鸡犬相闻。其中往来种作,男女衣着,悉如外人。黄发垂髫,并怡然自乐。 见渔人,乃大惊,问所从来。具答之。便要还家,设酒杀鸡作食。村中闻有此人,咸来问讯。自云先世避秦时乱,率妻子邑人来此绝境,不复出焉,遂与外人间隔。问今是何世,乃不知有汉,无论魏晋。此人一一为具言所闻,皆叹惋。余人各复延至其家,皆出酒食。停数日,辞去。此中人语云:“不足为外人道也。”(间隔 一作:隔绝) 既出,得其船,便扶向路,处处志之。及郡下,诣太守,说如此。太守即遣人随其往,寻向所志,遂迷,不复得路。 南阳刘子骥,高尚士也,闻之,欣然规往。未果,寻病终。后遂无问津者。
| DIR:/opt/alt/python27/lib/python2.7/site-packages/pip/_internal/utils/ |
| Current File : //opt/alt/python27/lib/python2.7/site-packages/pip/_internal/utils/parallel.py |
"""Convenient parallelization of higher order functions.
This module provides two helper functions, with appropriate fallbacks on
Python 2 and on systems lacking support for synchronization mechanisms:
- map_multiprocess
- map_multithread
These helpers work like Python 3's map, with two differences:
- They don't guarantee the order of processing of
the elements of the iterable.
- The underlying process/thread pools chop the iterable into
a number of chunks, so that for very long iterables using
a large value for chunksize can make the job complete much faster
than using the default value of 1.
"""
__all__ = ['map_multiprocess', 'map_multithread']
from contextlib import contextmanager
from multiprocessing import Pool as ProcessPool
from multiprocessing.dummy import Pool as ThreadPool
from pip._vendor.requests.adapters import DEFAULT_POOLSIZE
from pip._vendor.six import PY2
from pip._vendor.six.moves import map
from pip._internal.utils.typing import MYPY_CHECK_RUNNING
if MYPY_CHECK_RUNNING:
from typing import Callable, Iterable, Iterator, Union, TypeVar
from multiprocessing import pool
Pool = Union[pool.Pool, pool.ThreadPool]
S = TypeVar('S')
T = TypeVar('T')
# On platforms without sem_open, multiprocessing[.dummy] Pool
# cannot be created.
try:
import multiprocessing.synchronize # noqa
except ImportError:
LACK_SEM_OPEN = True
else:
LACK_SEM_OPEN = False
# Incredibly large timeout to work around bpo-8296 on Python 2.
TIMEOUT = 2000000
@contextmanager
def closing(pool):
# type: (Pool) -> Iterator[Pool]
"""Return a context manager making sure the pool closes properly."""
try:
yield pool
finally:
# For Pool.imap*, close and join are needed
# for the returned iterator to begin yielding.
pool.close()
pool.join()
pool.terminate()
def _map_fallback(func, iterable, chunksize=1):
# type: (Callable[[S], T], Iterable[S], int) -> Iterator[T]
"""Make an iterator applying func to each element in iterable.
This function is the sequential fallback either on Python 2
where Pool.imap* doesn't react to KeyboardInterrupt
or when sem_open is unavailable.
"""
return map(func, iterable)
def _map_multiprocess(func, iterable, chunksize=1):
# type: (Callable[[S], T], Iterable[S], int) -> Iterator[T]
"""Chop iterable into chunks and submit them to a process pool.
For very long iterables using a large value for chunksize can make
the job complete much faster than using the default value of 1.
Return an unordered iterator of the results.
"""
with closing(ProcessPool()) as pool:
return pool.imap_unordered(func, iterable, chunksize)
def _map_multithread(func, iterable, chunksize=1):
# type: (Callable[[S], T], Iterable[S], int) -> Iterator[T]
"""Chop iterable into chunks and submit them to a thread pool.
For very long iterables using a large value for chunksize can make
the job complete much faster than using the default value of 1.
Return an unordered iterator of the results.
"""
with closing(ThreadPool(DEFAULT_POOLSIZE)) as pool:
return pool.imap_unordered(func, iterable, chunksize)
if LACK_SEM_OPEN or PY2:
map_multiprocess = map_multithread = _map_fallback
else:
map_multiprocess = _map_multiprocess
map_multithread = _map_multithread
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