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Editing: parallel.cpython-310.pyc
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o ai| � @ sv d Z ddgZddlmZ ddlmZ ddlmZ ddlmZ ddl m Z m Z m Z mZmZ ddlmZ eejej f Zed �Zed �Zzdd lZW n eyS d ZY nw d ZdZedede e fdd��Z d de egef de e dede e fdd�Z d de egef de e dede e fdd�Z d de egef de e dede e fdd �Z er�e Z Z d S eZ 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��pool)�Callable�Iterable�Iterator�TypeVar�Union)�DEFAULT_POOLSIZE�S�TNTFi�� r �returnc c sB � z| V W | � � | �� | �� dS | � � | �� | �� w )z>Return a context manager making sure the pool closes properly.N)�close�join� terminater � r ��/builddir/build/BUILDROOT/alt-python310-pip-21.3.1-5.el8.x86_64/opt/alt/python310/lib/python3.10/site-packages/pip/_internal/utils/parallel.py�closing. s � � r � �func�iterable� chunksizec 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. )�map)r r r r r r � _map_fallback; s r c C s< t t� ��}|�| ||�W d � S 1 sw Y dS )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_multiprocessG s $�r c C s> t tt���}|�| ||�W d � S 1 sw Y dS )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 �_map_multithreadU s $�r" )r ) �__doc__�__all__� contextlibr �multiprocessingr r r Zmultiprocessing.dummyr! �typingr r r r r Z pip._vendor.requests.adaptersr r r Zmultiprocessing.synchronize� ImportErrorZ LACK_SEM_OPEN�TIMEOUTr �intr r r" r r r r r r �<module> sl � � ��� � � ��� �� ��� �
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