97 lines
3.1 KiB
Python
97 lines
3.1 KiB
Python
"""Utility functions.
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"""
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import contextlib
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import multiprocessing
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import sys
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from pathlib import Path
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from milc import cli
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maybe_exit_should_exit = True
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maybe_exit_reraise = False
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# Controls whether or not early `exit()` calls should be made
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def maybe_exit(rc):
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if maybe_exit_should_exit:
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sys.exit(rc)
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if maybe_exit_reraise:
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e = sys.exc_info()[1]
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if e:
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raise e
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def maybe_exit_config(should_exit: bool = True, should_reraise: bool = False):
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global maybe_exit_should_exit
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global maybe_exit_reraise
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maybe_exit_should_exit = should_exit
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maybe_exit_reraise = should_reraise
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def cached_get(*args, **kwargs):
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import requests_cache
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session = requests_cache.CachedSession(Path('~/.local/qmk/qmk_requests.sqlite').expanduser(), expire_after=300, cache_control=True)
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return session.get(*args, **kwargs)
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def download_with_progress(url, filename):
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import requests
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import tqdm
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response = requests.get(url, stream=True)
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total_size = int(response.headers.get('content-length', 0))
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with tqdm.tqdm(desc=filename, total=total_size, unit='B', unit_scale=True) as pbar:
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with open(filename, 'wb') as file:
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for data in response.iter_content(1024):
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file.write(data)
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pbar.update(len(data))
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@contextlib.contextmanager
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def parallelize():
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"""Returns a function that can be used in place of a map() call.
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Attempts to use `mpire`, falling back to `multiprocessing` if it's not
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available. If parallelization is not requested, returns the original map()
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function.
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"""
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# Work out if we've already got a config value for parallel searching
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if cli.config.user.parallel_search is None:
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parallel_search = True
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else:
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parallel_search = cli.config.user.parallel_search
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# Non-parallel searches use `map()`
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if not parallel_search:
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yield map
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return
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# Prefer mpire's `WorkerPool` if it's available
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with contextlib.suppress(ImportError):
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from mpire import WorkerPool
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from mpire.utils import make_single_arguments
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with WorkerPool() as pool:
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def _worker(func, *args):
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# Ensure we don't unpack tuples -- mpire's `WorkerPool` tries to do so normally so we tell it not to.
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for r in pool.imap_unordered(func, make_single_arguments(*args, generator=False), progress_bar=True):
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yield r
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yield _worker
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return
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# Otherwise fall back to multiprocessing's `Pool`
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with multiprocessing.Pool() as pool:
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yield pool.imap_unordered
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def parallel_map(*args, **kwargs):
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"""Effectively runs `map()` but executes it in parallel if necessary.
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"""
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with parallelize() as map_fn:
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# This needs to be enclosed in a `list()` as some implementations return
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# a generator function, which means the scope of the pool is closed off
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# before the results are returned. Returning a list ensures results are
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# materialised before any worker pool is shut down.
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return list(map_fn(*args, **kwargs))
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