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Edit File: parallel.pyc
� U��ic @ 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 d d l m Z m Z m Z m Z m Z e e j e j f Z e d � Z e d � Z n y d d l Z Wn e k re Z n Xe Z d Z e d � � Z d d � Z! d d � Z" d d � Z# e sfe rse! Z$ Z% n e" Z$ e# Z% d S( sb 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. t map_multiprocesst map_multithreadi����( t contextmanager( t Pool( t DEFAULT_POOLSIZE( t PY2( t map( t MYPY_CHECK_RUNNING( t pool( t Callablet Iterablet Iteratort TypeVart Uniont St TNi�� c c s/ z | VWd | j � | j � | j � Xd S( s> Return a context manager making sure the pool closes properly.N( t closet joint terminate( R ( ( sM /opt/alt/python27/lib/python2.7/site-packages/pip/_internal/utils/parallel.pyt closing4 s i c C s t | | � S( s� 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 ( t funct iterablet chunksize( ( sM /opt/alt/python27/lib/python2.7/site-packages/pip/_internal/utils/parallel.pyt _map_fallbackB s c C s/ t t � � � } | j | | | � SWd QXd S( s� 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 t ProcessPoolt imap_unordered( R R R R ( ( sM /opt/alt/python27/lib/python2.7/site-packages/pip/_internal/utils/parallel.pyt _map_multiprocessM s c C s2 t t t � � � } | j | | | � SWd QXd S( s� 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 t ThreadPoolR R ( R R R R ( ( sM /opt/alt/python27/lib/python2.7/site-packages/pip/_internal/utils/parallel.pyt _map_multithreadZ s (& t __doc__t __all__t contextlibR t multiprocessingR R t multiprocessing.dummyR t pip._vendor.requests.adaptersR t pip._vendor.sixR t pip._vendor.six.movesR t pip._internal.utils.typingR R t typingR R R R R R R t multiprocessing.synchronizet ImportErrort Truet LACK_SEM_OPENt Falset TIMEOUTR R R R R R ( ( ( sM /opt/alt/python27/lib/python2.7/site-packages/pip/_internal/utils/parallel.pyt <module> s8 (