marcocosta10000
6b3b6541a7
Initial project setup including the main agent script, configuration files, profile JSON state, systemd service unit, startup script, deployment documentation, and Python dependencies. Includes .env, venv/, and __pycache__/ which should likely be excluded via .gitignore to avoid committing secrets and artifacts.
260 lines
9.6 KiB
Python
260 lines
9.6 KiB
Python
"""
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Thin wrappers around `concurrent.futures`.
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"""
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import sys
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from contextlib import contextmanager
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from operator import length_hint
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from ..auto import tqdm as tqdm_auto
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from ..std import TqdmWarning
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__author__ = {"github.com/": ["casperdcl"]}
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__all__ = ['thread_map', 'process_map', 'interpreter_map']
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class _InterpreterLock:
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"""Reentrant lock backed by a cross-interpreter queue."""
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from threading import get_ident
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from time import monotonic as _time
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def __init__(self, queue):
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from threading import RLock
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self._queue = queue
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self._lock = RLock()
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self._owner = None
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self._depth = 0
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def acquire(self, blocking=True, timeout=-1):
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from queue import Empty
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start = self._time()
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if timeout == -1:
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acquired = self._lock.acquire(blocking)
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else:
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acquired = self._lock.acquire(blocking, timeout)
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if not acquired:
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return False
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if self._depth:
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self._depth += 1
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return True
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try:
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if not blocking:
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self._queue.get_nowait()
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elif timeout == -1:
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self._queue.get()
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else:
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remaining = max(0, timeout - (self._time() - start))
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self._queue.get(timeout=remaining)
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except Empty:
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self._lock.release()
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return False
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self._owner = self.get_ident()
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self._depth = 1
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return True
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def release(self):
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if self._owner != self.get_ident():
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raise RuntimeError("cannot release un-acquired lock")
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self._depth -= 1
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if not self._depth:
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self._owner = None
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self._queue.put(None)
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self._lock.release()
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def __enter__(self):
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self.acquire()
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return self
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def __exit__(self, *exc):
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self.release()
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@contextmanager
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def ensure_lock(tqdm_class, lock_name="", lock=None):
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"""get (create if necessary) and then restore `tqdm_class`'s lock"""
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old_lock = getattr(tqdm_class, '_lock', None) # don't create a new lock
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if lock is None:
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lock = old_lock or tqdm_class.get_lock() # maybe create a new lock
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lock = getattr(lock, lock_name, lock) # maybe subtype
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tqdm_class.set_lock(lock)
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yield lock
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if old_lock is None:
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del tqdm_class._lock
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else:
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tqdm_class.set_lock(old_lock)
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def _get_interpreter_init(tqdm_class, lock_queue_id):
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"""Return an initializer which bootstraps the parent import path and lock."""
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code = (
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"import sys\n"
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f"sys.path[:] = {sys.path!r}\n"
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"from concurrent import interpreters\n"
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"from importlib import import_module\n"
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"from tqdm.contrib.concurrent import _InterpreterLock\n"
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f"tqdm_class = import_module({tqdm_class.__module__!r})\n"
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f"for name in {tqdm_class.__qualname__.split('.')!r}:\n"
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" tqdm_class = getattr(tqdm_class, name)\n"
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"tqdm_class.monitor_interval = 0\n"
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f"tqdm_class.set_lock(_InterpreterLock(interpreters.Queue({lock_queue_id!r})))")
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return exec, (code,)
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def _min_map_len(iterables):
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"""min(map(length_hint, iterables), default=0)"""
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return min((n for it in iterables if (n := length_hint(it, -1)) >= 0), default=0)
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def _executor_map(
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PoolExecutor, fn, *iterables, max_workers=None, timeout=None, chunksize=1, lock_name="",
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tqdm_class=tqdm_auto, smoothing=0.0, _lock=None, _initializer=None, _initargs=None,
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**tqdm_kwargs
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):
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"""
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Implementation of `thread_map`, `process_map` and `interpreter_map`.
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Parameters
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----------
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max_workers : int
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timeout : int
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buffersize : int
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Requires Python>=3.14.
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thread_name_prefix : str
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max_tasks_per_child : int
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mp_context : str
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"""
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kwargs = tqdm_kwargs.copy()
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if 'total' not in kwargs:
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kwargs['total'] = _min_map_len(iterables)
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map_kwargs = {}
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if 'buffersize' in kwargs:
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map_kwargs['buffersize'] = kwargs.pop('buffersize')
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pool_kwargs = {}
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for k in ('thread_name_prefix', 'max_tasks_per_child', 'mp_context'):
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if k in kwargs:
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pool_kwargs[k] = kwargs.pop(k)
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dynamic_miniters = None
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if kwargs['total'] and 'miniters' not in kwargs:
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try:
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from os import process_cpu_count as cpu_count
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except ImportError:
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from os import cpu_count
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# thread & process pools have different default workers, but we KISS here
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rough_max = max_workers or min(32, (cpu_count() or 1) + 4)
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if kwargs['total'] > rough_max:
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kwargs['miniters'] = rough_max
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dynamic_miniters = True
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with ensure_lock(tqdm_class, lock_name=lock_name, lock=_lock) as lk:
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# share lock in case workers are already using `tqdm`
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if _initializer is None:
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_initializer = tqdm_class.set_lock
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_initargs = (lk,)
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with PoolExecutor(max_workers=max_workers, initializer=_initializer, initargs=_initargs,
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**pool_kwargs) as ex:
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with tqdm_class(smoothing=smoothing, **kwargs) as pbar:
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if dynamic_miniters is not None:
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pbar.dynamic_miniters = True
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orisubmit = ex.submit
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def patchsubmit(*args, **kwargs):
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fut = orisubmit(*args, **kwargs)
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fut.add_done_callback(lambda _: pbar.update())
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return fut
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ex.submit = patchsubmit
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return list(ex.map(
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fn, *iterables, timeout=timeout, chunksize=chunksize, **map_kwargs))
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def thread_map(fn, *iterables, **tqdm_kwargs):
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"""
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Equivalent of `list(map(fn, *iterables))`
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driven by `concurrent.futures.ThreadPoolExecutor`.
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Parameters
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----------
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max_workers : int, optional
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Maximum number of workers to spawn; passed to `concurrent.futures.ThreadPoolExecutor`.
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thread_name_prefix : str, optional
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Passed to `concurrent.futures.ThreadPoolExecutor` [default: ''].
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timeout : int or float, optional
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Seconds to wait before raising `TimeoutError` if `__next__` is called and the
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result isn't available. [default: None].
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buffersize : int, optional
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Requires Python>=3.14 [default: None].
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tqdm_class : optional
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`tqdm` class to use for bars [default: tqdm.auto.tqdm].
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smoothing : float, optional
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Passed to `tqdm_class`; the [default: 0] is average (due to erratic update frequency).
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lock_name : str, optional
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Member of `tqdm_class.get_lock()` to use [default: ''].
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"""
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from concurrent.futures import ThreadPoolExecutor
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return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)
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def interpreter_map(fn, *iterables, **tqdm_kwargs):
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"""
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Equivalent of `list(map(fn, *iterables))`
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driven by `concurrent.futures.InterpreterPoolExecutor` (Python 3.14+).
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Parameters
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----------
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Same as `thread_map`.
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Notes
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-----
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`fn`, its arguments, and its return values must be pickleable.
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Worker progress bars using the same `tqdm_class` share a cross-interpreter write lock.
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"""
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from concurrent import interpreters
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from concurrent.futures import InterpreterPoolExecutor
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lock_queue = interpreters.create_queue()
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lock_queue.put(None)
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tqdm_class = tqdm_kwargs.get("tqdm_class", tqdm_auto)
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initializer, initargs = _get_interpreter_init(tqdm_class, lock_queue.id)
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return _executor_map(
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InterpreterPoolExecutor, fn, *iterables, _lock=_InterpreterLock(lock_queue),
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_initializer=initializer, _initargs=initargs, **tqdm_kwargs)
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def process_map(fn, *iterables, lock_name="mp_lock", **tqdm_kwargs):
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"""
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Equivalent of `list(map(fn, *iterables))`
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driven by `concurrent.futures.ProcessPoolExecutor`.
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Parameters
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----------
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max_workers : int, optional
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Maximum number of workers to spawn; passed to `concurrent.futures.ProcessPoolExecutor`.
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timeout : int or float, optional
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Seconds to wait before raising `TimeoutError` if `__next__` is called and the
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result isn't available. [default: None].
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chunksize : int, optional
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Approximate size of chunks sent to worker processes; passed to
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`concurrent.futures.ProcessPoolExecutor.map`. [default: 1].
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buffersize : int, optional
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Requires Python>=3.14 [default: None].
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max_tasks_per_child : int, optional
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Maximum number of tasks a worker process can complete before being replaced
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with a new process; passed to `concurrent.futures.ProcessPoolExecutor`.
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mp_context : multiprocessing.BaseContext, optional
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Multiprocessing context to use, e.g. `multiprocessing.get_context('fork')`.
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lock_name : str, optional
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Member of `tqdm_class.get_lock()` to use [default: mp_lock].
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tqdm_class : optional
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`tqdm` class to use for bars [default: tqdm.auto.tqdm].
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smoothing : float, optional
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Passed to `tqdm_class`; the [default: 0] is average (due to erratic update frequency).
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"""
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from concurrent.futures import ProcessPoolExecutor
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if iterables and 'chunksize' not in tqdm_kwargs:
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# default `chunksize=1` has poor performance for large iterables
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# (most time spent dispatching items to workers).
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shortest_iterable_len = _min_map_len(iterables)
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if shortest_iterable_len > 1000:
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from warnings import warn
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warn("Iterable length %d > 1000 but `chunksize` is not set."
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" This may seriously degrade multiprocess performance."
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" Set `chunksize=1` or more." % shortest_iterable_len,
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TqdmWarning, stacklevel=2)
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return _executor_map(ProcessPoolExecutor, fn, *iterables, lock_name=lock_name, **tqdm_kwargs)
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