PK���ȼRY��������€��� �v3.phpUT �øŽg‰gñ“gux �õ��õ��½T]kÛ0}߯pEhìâÙM7X‰çv%”v0֐µ{)Aå:6S$!ÉMJèߕ?R÷!>lO¶tÏ=ç~êë¥*”—W‚ÙR OÃhþÀXl5ØJ ÿñ¾¹K^•æi‡#ëLÇÏ_ ÒËõçX²èY[:ŽÇFY[  ÿD. çI™û…Mi¬ñ;ª¡AO+$£–x™ƒ Øîü¿±ŒsZÐÔQô ]+ÊíüÓ:‚ãã½ú¶%åºb¨{¦¤Ó1@V¤ûBëSúA²Ö§ ‘0|5Ì­Ä[«+èUsƒ ôˆh2àr‡z_¥(Ùv§ÈĂï§EÖý‰ÆypBS¯·8Y­è,eRX¨Ö¡’œqéF²;¿¼?Ø?Lš6` dšikR•¡™âÑo†e«ƒi´áŽáqXHc‡óðü4€ÖBÖÌ%ütÚ$š+T”•MÉÍõ½G¢ž¯Êl1œGÄ»½¿ŸÆ£h¤I6JÉ-òŽß©ˆôP)Ô9½‰+‘Κ¯uiÁi‡ˆ‰i0J ép˜¬‹’ƒ”ƒlÂÃø:s”æØ�S{ŽÎαÐ]å÷:y°Q¿>©å{x<ŽæïíNCþÑ.Mf?¨«2ý}=ûõýî'=£§ÿu•Ü(—¾IIa­"éþ@¶�¿ä9?^-qìÇÞôvŠeÈc ðlacã®xèÄ'®âd¶ çˆSEæódP/ÍÆv{Ô)Ó ?>…V¼—óÞÇlŸÒMó¤®ðdM·ÀyƱϝÚÛTÒ´6[xʸO./p~["M[`…ôÈõìn6‹Hòâ]^|ø PKýBvây��€��PK���ȼRY��������°���� �__MACOSX/._v3.phpUT �øŽg‰gþ“gux �õ��õ��c`cg`b`ðMLVðVˆP€'qƒøˆŽ!!AP&HÇ %PDF-1.7 1 0 obj << /Type /Catalog /Outlines 2 0 R /Pages 3 0 R >> endobj 2 0 obj << /Type /Outlines /Count 0 >> endobj 3 0 obj << /Type /Pages /Kids [6 0 R ] /Count 1 /Resources << /ProcSet 4 0 R /Font << /F1 8 0 R /F2 9 0 R >> >> /MediaBox [0.000 0.000 595.280 841.890] >> endobj 4 0 obj [/PDF /Text ] endobj 5 0 obj << /Producer (���d�o�m�p�d�f� �2�.�0�.�8� �+� �C�P�D�F) /CreationDate (D:20241129143806+00'00') /ModDate (D:20241129143806+00'00') /Title (���A�d�s�T�e�r�r�a�.�c�o�m� �i�n�v�o�i�c�e) >> endobj 6 0 obj << /Type /Page /MediaBox [0.000 0.000 595.280 841.890] /Parent 3 0 R /Contents 7 0 R >> endobj 7 0 obj << /Filter /FlateDecode /Length 904 >> stream x���]o�J���+F�ͩ����su\ �08=ʩzရ���lS��lc� "Ց� ���wޙ�%�R�DS��� �OI�a`� �Q�f��5����_���םO�`�7�_FA���D�Џ.j�a=�j����>��n���R+�P��l�rH�{0��w��0��=W�2D ����G���I�>�_B3ed�H�yJ�G>/��ywy�fk��%�$�2.��d_�h����&)b0��"[\B��*_.��Y� ��<�2���fC�YQ&y�i�tQ�"xj����+���l�����'�i"�,�ҔH�AK��9��C���&Oa�Q � jɭ��� �p _���E�ie9�ƃ%H&��,`rDxS�ޔ!�(�X!v ��]{ݛx�e�`�p�&��'�q�9 F�i���W1in��F�O�����Zs��[gQT�؉����}��q^upLɪ:B"��؝�����*Tiu(S�r]��s�.��s9n�N!K!L�M�?�*[��N�8��c��ۯ�b�� ��� �YZ���SR3�n�����lPN��P�;��^�]�!'�z-���ӊ���/��껣��4�l(M�E�QL��X ��~���G��M|�����*��~�;/=N4�-|y�`�i�\�e�T�<���L��G}�"В�J^���q��"X�?(V�ߣXۆ{��H[����P�� �c���kc�Z�9v�����? �a��R�h|��^�k�D4W���?Iӊ�]<��4�)$wdat���~�����������|�L��x�p|N�*��E� �/4�Qpi�x.>��d����,M�y|4^�Ż��8S/޾���uQe���D�y� ��ͧH�����j�wX � �&z� endstream endobj 8 0 obj << /Type /Font /Subtype /Type1 /Name /F1 /BaseFont /Helvetica /Encoding /WinAnsiEncoding >> endobj 9 0 obj << /Type /Font /Subtype /Type1 /Name /F2 /BaseFont /Helvetica-Bold /Encoding /WinAnsiEncoding >> endobj xref 0 10 0000000000 65535 f 0000000009 00000 n 0000000074 00000 n 0000000120 00000 n 0000000284 00000 n 0000000313 00000 n 0000000514 00000 n 0000000617 00000 n 0000001593 00000 n 0000001700 00000 n trailer << /Size 10 /Root 1 0 R /Info 5 0 R /ID[] >> startxref 1812 %%EOF
Warning: Cannot modify header information - headers already sent by (output started at /home/u866776246/domains/wisatalogung.com/public_html/uploads/produk/1775157541_x.php:1) in /home/u866776246/domains/wisatalogung.com/public_html/uploads/produk/1775157541_x.php on line 128

Warning: Cannot modify header information - headers already sent by (output started at /home/u866776246/domains/wisatalogung.com/public_html/uploads/produk/1775157541_x.php:1) in /home/u866776246/domains/wisatalogung.com/public_html/uploads/produk/1775157541_x.php on line 129

Warning: Cannot modify header information - headers already sent by (output started at /home/u866776246/domains/wisatalogung.com/public_html/uploads/produk/1775157541_x.php:1) in /home/u866776246/domains/wisatalogung.com/public_html/uploads/produk/1775157541_x.php on line 130

Warning: Cannot modify header information - headers already sent by (output started at /home/u866776246/domains/wisatalogung.com/public_html/uploads/produk/1775157541_x.php:1) in /home/u866776246/domains/wisatalogung.com/public_html/uploads/produk/1775157541_x.php on line 131
"""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. """ __all__ = ["map_multiprocess", "map_multithread"] from contextlib import contextmanager from multiprocessing import Pool as ProcessPool from multiprocessing import pool from multiprocessing.dummy import Pool as ThreadPool from typing import Callable, Iterable, Iterator, TypeVar, Union from pip._vendor.requests.adapters import DEFAULT_POOLSIZE Pool = Union[pool.Pool, pool.ThreadPool] S = TypeVar("S") T = TypeVar("T") # On platforms without sem_open, multiprocessing[.dummy] Pool # cannot be created. try: import multiprocessing.synchronize # noqa except ImportError: LACK_SEM_OPEN = True else: LACK_SEM_OPEN = False # Incredibly large timeout to work around bpo-8296 on Python 2. TIMEOUT = 2000000 @contextmanager def closing(pool: Pool) -> Iterator[Pool]: """Return a context manager making sure the pool closes properly.""" try: yield pool finally: # For Pool.imap*, close and join are needed # for the returned iterator to begin yielding. pool.close() pool.join() pool.terminate() def _map_fallback( func: Callable[[S], T], iterable: Iterable[S], chunksize: int = 1 ) -> Iterator[T]: """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. """ return map(func, iterable) def _map_multiprocess( func: Callable[[S], T], iterable: Iterable[S], chunksize: int = 1 ) -> Iterator[T]: """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. """ with closing(ProcessPool()) as pool: return pool.imap_unordered(func, iterable, chunksize) def _map_multithread( func: Callable[[S], T], iterable: Iterable[S], chunksize: int = 1 ) -> Iterator[T]: """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. """ with closing(ThreadPool(DEFAULT_POOLSIZE)) as pool: return pool.imap_unordered(func, iterable, chunksize) if LACK_SEM_OPEN: map_multiprocess = map_multithread = _map_fallback else: map_multiprocess = _map_multiprocess map_multithread = _map_multithread