a tDf @sUdZddlmZddlZddlZddlZddlZddlm Z ddl m Z m Z ddl mZmZddlmZmZddlmZdd lmZmZdd lmZdd lmZd d ZeZdaded<e eZ ded<eZ!ddZ"ddddddZ#dS)z2 A threaded shared-memory scheduler See local.py ) annotationsN) defaultdict)MappingSequence)ExecutorThreadPoolExecutor)Lockcurrent_thread)config)MultiprocessingPoolExecutor get_async) CPU_COUNT)KeycCstjS)N)r identrrZ/nfs/NAS7/SABIOD/METHODE/ermites/ermites_venv/lib/python3.9/site-packages/dask/threaded.py_thread_get_idsrzExecutor | None default_poolz2defaultdict[threading.Thread, dict[int, Executor]]poolscCs|tdfS)N)sysexc_info)edumpsrrrpack_exception!srrzSequence[Key] | Key)dskkeysc Ksx|ptdd}|ptdd}t}t|dur|durf|turftdur`ttat tj t}q|t vr|t |vrt ||}qt|}t |j |t ||<nt |t jjrt|}Wdn1s0Yt|j|j||f|ttd|}t^tt}|turTtt D].} | |vr$t | D]} | q@q$Wdn1sj0Y|S)aThreaded cached implementation of dask.get Parameters ---------- dsk: dict A dask dictionary specifying a workflow keys: key or list of keys Keys corresponding to desired data num_workers: integer of thread count The number of threads to use in the ThreadPool that will actually execute tasks cache: dict-like (optional) Temporary storage of results Examples -------- >>> inc = lambda x: x + 1 >>> add = lambda x, y: x + y >>> dsk = {'x': 1, 'y': 2, 'z': (inc, 'x'), 'w': (add, 'z', 'y')} >>> get(dsk, 'w') 4 >>> get(dsk, ['w', 'y']) (4, 2) poolN num_workers)cacheZget_idr)r getr pools_lock main_threadrrr atexitregistershutdownr isinstancemultiprocessingrZPoolr r ZsubmitZ _max_workersrrset threading enumeratelistpopvalues) rrrrrkwargsthreadresultsZactive_threadstprrrr %sJ!  &     0r )NNN)$__doc__ __future__rr#Zmultiprocessing.poolr'rr) collectionsrcollections.abcrrconcurrent.futuresrrrr Zdaskr Z dask.localr r Z dask.systemr Z dask.typingrrr"r__annotations__dictrr!rr rrrrs.