j ddlZddlZddlmZmZmZddlmZddl m Z ddl m Z dgZ d dee ge fdee d edzd ee fd ZdS) N)CallableIterableIterator) ThreadPool)Any)ENV_VARS_TRUE_VALUESmultithread_execfuncseqthreadsreturnct|tr|n tdtj}|dks9t jddtvrt||}nLt|5}td| ||}dddn #1swxYwY|S)aOExecute a given function in parallel for each element of a given sequence >>> from doctr.utils.multithreading import multithread_exec >>> entries = [1, 4, 8] >>> results = multithread_exec(lambda x: x ** 2, entries) Args: func: function to be executed on each element of the iterable seq: iterable threads: number of workers to be used for multiprocessing Returns: iterator of the function's results using the iterable as inputs Notes: This function uses ThreadPool from multiprocessing package, which uses `/dev/shm` directory for shared memory. If you do not have write permissions for this directory (if you run `doctr` on AWS Lambda for instance), you might want to disable multiprocessing. To achieve that, set 'DOCTR_MULTIPROCESSING_DISABLE' to 'TRUE'. DOCTR_MULTIPROCESSING_DISABLEc|SN)xs ]/var/www/html/Carbon-Document/venv/lib/python3.11/site-packages/doctr/utils/multithreading.pyz"multithread_exec../sAN) isinstanceintminmp cpu_countosenvirongetupperrmapr)r r r resultstps rr r s($GS11Nggs2r|~~7N7NG{{bjnn%DbIIOOQQUiiidC..  :B++rvvdC'8'899G : : : : : : : : : : : : : : : Ns&C  CCr)multiprocessingrrcollections.abcrrrmultiprocessing.poolrtypingrdoctr.file_utilsr__all__rr rrrr,s 8888888888++++++111111  8SE3J/hsmcTXjdlmpdqr