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Editing: parallel.cpython-35.pyc
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��ReL � @ s� d Z d d g Z d d l m Z d d l m Z d d l m Z d d l m Z d d l m Z d d l m Z d d l m Z e r� d d l m Z m Z m Z m Z m Z d d l m Z e e j e j f Z e d � Z e d � Z y d d l Z Wn e k rd Z Yn Xd Z d Z e d d � � Z d d d � Z d d d � Z d d d � Z! e spe r}e Z" Z# n e Z" e! Z# d S)ab 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. �map_multiprocess�map_multithread� )�contextmanager)�Pool)�DEFAULT_POOLSIZE)�PY2)�map)�MYPY_CHECK_RUNNING)�Callable�Iterable�Iterator�Union�TypeVar)�pool�S�TNTFi�� c c s/ z | VWd | j � | j � | j � Xd S)z>Return a context manager making sure the pool closes properly.N)�close�join� terminate)r � r ��/builddir/build/BUILDROOT/alt-python35-pip-20.2.4-5.el8.x86_64/opt/alt/python35/lib/python3.5/site-packages/pip/_internal/utils/parallel.py�closing4 s r � c C s t | | � S)z�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. )r )�func�iterable� chunksizer r r � _map_fallbackB s r c C s0 t t � � � } | j | | | � SWd QRXd S)z�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. N)r � ProcessPool�imap_unordered)r r r r r r r �_map_multiprocessM s r c C s3 t t t � � � } | j | | | � SWd QRXd S)z�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. N)r � ThreadPoolr r )r r r r r r r �_map_multithreadZ s r! )$�__doc__�__all__� contextlibr �multiprocessingr r Zmultiprocessing.dummyr Z pip._vendor.requests.adaptersr Zpip._vendor.sixr �pip._vendor.six.movesr �pip._internal.utils.typingr �typingr r r r r r r r Zmultiprocessing.synchronize� ImportErrorZ LACK_SEM_OPEN�TIMEOUTr r r r! r r r r r r �<module> s8 (
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