jupytersketcher/Lib/site-packages/tqdm/contrib/concurrent.py

"""
Thin wrappers around `concurrent.futures`.
"""
from __future__ import absolute_import
from tqdm import TqdmWarning
from tqdm.auto import tqdm as tqdm_auto
try:
    from operator import length_hint
except ImportError:
    def length_hint(it, default=0):
        """Returns `len(it)`, falling back to `default`"""
        try:
            return len(it)
        except TypeError:
            return default
try:
    from os import cpu_count
except ImportError:
    try:
        from multiprocessing import cpu_count
    except ImportError:
        def cpu_count():
            return 4
import sys
__author__ = {"github.com/": ["casperdcl"]}
__all__ = ['thread_map', 'process_map']


def _executor_map(PoolExecutor, fn, *iterables, **tqdm_kwargs):
    """
    Implementation of `thread_map` and `process_map`.

    Parameters
    ----------
    tqdm_class  : [default: tqdm.auto.tqdm].
    max_workers  : [default: min(32, cpu_count() + 4)].
    chunksize  : [default: 1].
    """
    kwargs = tqdm_kwargs.copy()
    if "total" not in kwargs:
        kwargs["total"] = len(iterables[0])
    tqdm_class = kwargs.pop("tqdm_class", tqdm_auto)
    max_workers = kwargs.pop("max_workers", min(32, cpu_count() + 4))
    chunksize = kwargs.pop("chunksize", 1)
    pool_kwargs = dict(max_workers=max_workers)
    sys_version = sys.version_info[:2]
    if sys_version >= (3, 7):
        # share lock in case workers are already using `tqdm`
        pool_kwargs.update(
            initializer=tqdm_class.set_lock, initargs=(tqdm_class.get_lock(),))
    map_args = {}
    if not (3, 0) < sys_version < (3, 5):
        map_args.update(chunksize=chunksize)
    with PoolExecutor(**pool_kwargs) as ex:
        return list(tqdm_class(
            ex.map(fn, *iterables, **map_args), **kwargs))


def thread_map(fn, *iterables, **tqdm_kwargs):
    """
    Equivalent of `list(map(fn, *iterables))`
    driven by `concurrent.futures.ThreadPoolExecutor`.

    Parameters
    ----------
    tqdm_class : optional
        `tqdm` class to use for bars [default: tqdm.auto.tqdm].
    max_workers : int, optional
        Maximum number of workers to spawn; passed to
        `concurrent.futures.ThreadPoolExecutor.__init__`.
        [default: max(32, cpu_count() + 4)].
    """
    from concurrent.futures import ThreadPoolExecutor
    return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)


def process_map(fn, *iterables, **tqdm_kwargs):
    """
    Equivalent of `list(map(fn, *iterables))`
    driven by `concurrent.futures.ProcessPoolExecutor`.

    Parameters
    ----------
    tqdm_class  : optional
        `tqdm` class to use for bars [default: tqdm.auto.tqdm].
    max_workers : int, optional
        Maximum number of workers to spawn; passed to
        `concurrent.futures.ProcessPoolExecutor.__init__`.
        [default: min(32, cpu_count() + 4)].
    chunksize : int, optional
        Size of chunks sent to worker processes; passed to
        `concurrent.futures.ProcessPoolExecutor.map`. [default: 1].
    """
    from concurrent.futures import ProcessPoolExecutor
    if iterables and "chunksize" not in tqdm_kwargs:
        # default `chunksize=1` has poor performance for large iterables
        # (most time spent dispatching items to workers).
        longest_iterable_len = max(map(length_hint, iterables))
        if longest_iterable_len > 1000:
            from warnings import warn
            warn("Iterable length %d > 1000 but `chunksize` is not set."
                 " This may seriously degrade multiprocess performance."
                 " Set `chunksize=1` or more." % longest_iterable_len,
                 TqdmWarning, stacklevel=2)
    return _executor_map(ProcessPoolExecutor, fn, *iterables, **tqdm_kwargs)