£Á°èZ¨Ä…–K§‚«“ô4“ÒÙ´dîfUÙÃÅ WKbyÊ¦•êŽ…È®FÒ¿ÊÎóCozá¬S@6{Í:›œêZÌ:Š•_%:¢¾¾~;‘Ã~èŠ©ÊÇí`ÔÑ©úë™µ'5I¿fš×WO%ø9¾«¾DK|€ùÍD”Ýs]nHÕ¶ê×Ó¼ãžªéUWŸÈË%DÒÕ¬ï‘]/Åcx ‰ï2ß]ä6G[]S£ÔÏ¯rs{úëóµmÒï#UQxo·õÞCe]"±/aÙ&Eã4ú9Jé_ÞåëdãöKë)AÞ ¯¹ægƒÛowÐø^d™ý½ßB7áyMä9ÜÖUã !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! a ��Re|�@s\dZddgZddlmZddlmZddlmZddlmZ ddl mZmZm Z mZmZddlmZeejej fZed �Zed �ZzddlZWney�dZYn0d ZdZeee ed�dd��Zdeegefeeee ed�dd�Zdeegefeeee ed�dd�Zdeegefeeee ed�dd�Ze�rPeZZneZeZdS)abConvenient 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��pool)�Callable�Iterable�Iterator�TypeVar�Union)�DEFAULT_POOLSIZE�S�TNTFi��)r�returnccsBz"|VW|��|��|��n|��|��|��0dS)z>Return a context manager making sure the pool closes properly.N)�close�join� terminater�r��/builddir/build/BUILDROOT/alt-python39-pip-21.3.1-2.el8.x86_64/opt/alt/python39/lib/python3.9/site-packages/pip/_internal/utils/parallel.py�closing.s �r�)�func�iterable� chunksizercCs 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. )�map)rrrrrr� _map_fallback;s rcCs<tt��}|�|||�Wd�S1s.0YdS)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�rrrrrrr�_map_multiprocessGs r cCs>ttt��}|�|||�Wd�S1s00YdS)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 rrrrr�_map_multithreadUs r")r)r)r)�__doc__�__all__� contextlibr�multiprocessingrrrZmultiprocessing.dummyr!�typingrr r rrZpip._vendor.requests.adaptersr rrZmultiprocessing.synchronize�ImportErrorZ LACK_SEM_OPEN�TIMEOUTr�intrr r"rrrrrr�sJ ��