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e dddZ erPe Z Z neZ 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_multiprocessmap_multithread )contextmanager)Poolpool)CallableIterableIteratorTypeVarUnion)DEFAULT_POOLSIZESTNTFi )r returnc c sB z"| V W | | | n| | | 0 dS )z>Return a context manager making sure the pool closes properly.N)closejoinZ terminater r @/usr/lib/python3.9/site-packages/pip/_internal/utils/parallel.pyclosing. s
r )funciterable chunksizer c C s
t | |S )zMake 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 s.0 Y dS )zChop 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 ProcessPoolimap_unorderedr r r r r r r _map_multiprocessG s
r c C s> t tt }|| ||W d S 1 s00 Y dS )zChop 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 )r )r ) __doc____all__
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