import multiprocessing
import random
import time
import math
import tqdm

results = []

def task(position, lock):
	total_iterations = 10
	process_idx = multiprocessing.current_process()._identity[0]

	with lock:
		bar = tqdm.tqdm(
			desc=f'Position {process_idx}',
			total=total_iterations,
			position=process_idx,
			leave=False
		)

	for _ in range(total_iterations):
		time.sleep(random.uniform(0.01, 0.4))
		with lock:
			bar.update(1)

	with lock:
		bar.close()
	return position

def store_result(result):
	results.append(result)

if __name__ == '__main__':
	lock = multiprocessing.Manager().Lock()
	
	pool_size = math.floor(multiprocessing.cpu_count()/2)

	with multiprocessing.Pool(pool_size) as pool:
		for position in range(20):
			pool.apply_async(task, args = (position + 1, lock), callback=store_result)

		pool.close()
		pool.join()
	
	print(results)