JFIF$        dd7 

Viewing File: /usr/lib/python3.9/site-packages/pip/_internal/utils/__pycache__/parallel.cpython-39.opt-1.pyc

a

[��f|�@s\dZddgZddlmZddlmZddlmZddlmZ	ddl
mZmZm
Z
mZmZddlmZeejej	fZed	�Zed
�ZzddlZWney�dZYn0d
ZdZeee
ed�dd��Zdeegefeeee
ed�dd�Zdeegefeeee
ed�dd�Zdeegefeeee
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�joinZ	terminater�r�@/usr/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
rcCs>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__�
contextlibrZmultiprocessingrrrZmultiprocessing.dummyr �typingrr	r
rrZpip._vendor.requests.adaptersr
rrZmultiprocessing.synchronize�ImportErrorZ
LACK_SEM_OPENZTIMEOUTr�intrrr!rrrrrr�<module>sJ

��
����

Back to Directory  nL+D550H?Mx ,D"v]qv;6*Zqn)ZP0!1 A "#a$2Qr D8 a Ri[f\mIykIw0cuFcRı?lO7к_f˓[C$殷WF<_W ԣsKcëIzyQy/_LKℂ;C",pFA:/]=H  ~,ls/9ć:[=/#f;)x{ٛEQ )~ =𘙲r*2~ a _V=' kumFD}KYYC)({ *g&f`툪ry`=^cJ.I](*`wq1dđ#̩͑0;H]u搂@:~וKL Nsh}OIR*8:2 !lDJVo(3=M(zȰ+i*NAr6KnSl)!JJӁ* %݉?|D}d5:eP0R;{$X'xF@.ÊB {,WJuQɲRI;9QE琯62fT.DUJ;*cP A\ILNj!J۱+O\͔]ޒS߼Jȧc%ANolՎprULZԛerE2=XDXgVQeӓk yP7U*omQIs,K`)6\G3t?pgjrmۛجwluGtfh9uyP0D;Uڽ"OXlif$)&|ML0Zrm1[HXPlPR0'G=i2N+0e2]]9VTPO׮7h(F*癈'=QVZDF,d߬~TX G[`le69CR(!S2!P <0x<!1AQ "Raq02Br#SCTb ?Ζ"]mH5WR7k.ۛ!}Q~+yԏz|@T20S~Kek *zFf^2X*(@8r?CIuI|֓>^ExLgNUY+{.RѪ τV׸YTD I62'8Y27'\TP.6d&˦@Vqi|8-OΕ]ʔ U=TL8=;6c| !qfF3aů&~$l}'NWUs$Uk^SV:U# 6w++s&r+nڐ{@29 gL u"TÙM=6(^"7r}=6YݾlCuhquympǦ GjhsǜNlɻ}o7#S6aw4!OSrD57%|?x>L |/nD6?/8w#[)L7+6〼T ATg!%5MmZ/c-{1_Je"|^$'O&ޱմTrb$w)R$& N1EtdU3Uȉ1pM"N*(DNyd96.(jQ)X 5cQɎMyW?Q*!R>6=7)Xj5`J]e8%t!+'!1Q5 !1 AQaqё#2"0BRb?Gt^## .llQT $v,,m㵜5ubV =sY+@d{N! dnO<.-B;_wJt6;QJd.Qc%p{ 1,sNDdFHI0ГoXшe黅XۢF:)[FGXƹ/w_cMeD,ʡcc.WDtA$j@:) -# u c1<@ۗ9F)KJ-hpP]_x[qBlbpʖw q"LFGdƶ*s+ډ_Zc"?%t[IP 6J]#=ɺVvvCGsGh1 >)6|ey?Lӣm,4GWUi`]uJVoVDG< SB6ϏQ@ TiUlyOU0kfV~~}SZ@*WUUi##; s/[=!7}"WN]'(L! ~y5g9T̅JkbM' +s:S +B)v@Mj e Cf jE 0Y\QnzG1д~Wo{T9?`Rmyhsy3!HAD]mc1~2LSu7xT;j$`}4->L#vzŏILS ֭T{rjGKC;bpU=-`BsK.SFw4Mq]ZdHS0)tLg