U
    
Ha                     @   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mZmZmZmZ ddlmZ eejej	f Zed	Zed
ZzddlZW n ek
r   dZY nX dZdZedd ZdddZdddZdddZere ZZneZ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 c                 c   s*   z
| V  W 5 |    |   |   X dS )z>Return a context manager making sure the pool closes properly.N)closejoin	terminater    r   @/tmp/pip-unpacked-wheel-tx790h60/pip/_internal/utils/parallel.pyclosing.   s
    
r      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)funciterable	chunksizer   r   r   _map_fallback<   s    r   c              
   C   s0   t t }|| ||W  5 Q R  S Q R X dS )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   ProcessPoolimap_unorderedr   r   r   r   r   r   r   _map_multiprocessG   s    	r   c              
   C   s2   t tt}|| ||W  5 Q R  S Q R X dS )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_multithreadT   s    	r!   )r   )r   )r   )__doc____all__
contextlibr   multiprocessingr   r   r   Zmultiprocessing.dummyr    typingr   r	   r
   r   r   pip._vendor.requests.adaptersr   r   r   Zmultiprocessing.synchronizeImportErrorZLACK_SEM_OPENTIMEOUTr   r   r   r!   r   r   r   r   r   r   <module>   s2   




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