Lg  UdZddlmZddlZddlZddlZddlZddlm Z ddl m Z m Z ddl mZmZddlmZmZddlmZdd lmZmZdd lmZdd lmZd ZeZdad ed<e eZ ded<eZ!dZ" d ddZ#y)z2 A threaded shared-memory scheduler See local.py ) annotationsN) defaultdict)MappingSequence)ExecutorThreadPoolExecutor)Lockcurrent_thread)config)MultiprocessingPoolExecutor get_async) CPU_COUNT)Keyc*tjS)N)r ident-lib/python3.12/site-packages/dask/threaded.py_thread_get_idrs   ! !!rzExecutor | None default_poolz2defaultdict[threading.Thread, dict[int, Executor]]poolsc4|tjdfS)N)sysexc_info)edumpss rpack_exceptionr"s cllnQ rc |xstjdd}|xstjdd}t}t5||G|tur?t 2t tatjt jt }n|tvr|t|vr t||}nft |}tj|j|t||<n/t|tjjr t!|}dddt#|j$|j&||f|t(t*d|}t5t-t/j0}|turQt3tD]?} | |vstj5| j7D]} | jAddd|S#1swYxYw#1swY|SxYw)aThreaded cached implementation of dask.get Parameters ---------- dsk: dict A dask dictionary specifying a workflow keys: key or list of keys Keys corresponding to desired data num_workers: integer of thread count The number of threads to use in the ThreadPool that will actually execute tasks cache: dict-like (optional) Temporary storage of results Examples -------- >>> inc = lambda x: x + 1 >>> add = lambda x, y: x + y >>> dsk = {'x': 1, 'y': 2, 'z': (inc, 'x'), 'w': (add, 'z', 'y')} >>> get(dsk, 'w') 4 >>> get(dsk, ['w', 'y']) (4, 2) poolN num_workers)cacheget_idr)r getr pools_lock main_threadrrratexitregistershutdownr isinstancemultiprocessingr Poolr r submit _max_workersrrset threading enumeratelistpopvalues) dskkeysr"r!r kwargsthreadresultsactive_threadstps rr$r$&sB  +6::fd+D@M4!@K  F  <"v'<'#5i#@LOOL$9$9:#5[E&M%AV}[1)+6 .-1f k* o2277 8.t4D       %   G Y0023  $%[N*"YYq\002 3!  NI 8  NsCGrIs #  #-;*=!" $ o$