Parallelize any python function

parallelize.py

from concurrent.futures import ThreadPoolExecutor, Future
from collections.abc import Callable, Iterable, Sized
from typing import TypeVar
import os

from tqdm import tqdm

T = TypeVar('T')
R = TypeVar('R')

def parallelize(func: Callable[[T], R], iterable: Iterable[T], max_workers: int | None, *args) -> list[R]:
    """
    Parallelize a function that receives an item from an iterable and returns a result.
    Args:
        func: Function to be parallelized
        iterable: Iterable to be used as input for the function
        max_workers: Number of threads to be used
        args: Additional arguments to be passed to the function
    Return:
        List of results with the same type as the function return
    """
    futures: list[Future[R]] = []
    # If iterable has a length, use tqdm parameter to show progress
    if isinstance(iterable, Sized):
        pbar = tqdm(total=len(iterable))
    else:
        pbar = tqdm()

    cpu_count: int = max_workers if max_workers is not None else len(os.sched_getaffinity(0))

    with ThreadPoolExecutor(max_workers=cpu_count) as executor:
        for item in iterable:
            future = executor.submit(func, item, *args)
            future.add_done_callback(lambda p: pbar.update(1))
            futures.append(future)

    return [future.result() for future in futures]