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task_id stringlengths 11 13	prompt stringlengths 115 1.36k	canonical_solution stringlengths 19 865	entry_point stringlengths 1 30	test stringlengths 928 502k	question stringlengths 0 1.23k	starter_code stringlengths 10 775
HumanEval/0	from typing import List def has_close_elements(numbers: List[float], threshold: float) -> bool: """ Check if in given list of numbers, are any two numbers closer to each other than given threshold. >>> has_close_elements([1.0, 2.0, 3.0], 0.5) False >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3) True """	sorted_numbers = sorted(numbers) for i in range(len(sorted_numbers) - 1): if sorted_numbers[i + 1] - sorted_numbers[i] < threshold: return True return False	has_close_elements	import numpy as np def is_floats(x) -> bool: # check if it is float; List[float]; Tuple[float] if isinstance(x, float): return True if isinstance(x, (list, tuple)): return all(isinstance(i, float) for i in x) if isinstance(x, np.ndarray): return x.dtype == np.float64 or x.dtype == np.float32 return False def assertion(out, exp, atol): exact_match = out == exp if atol == 0 and is_floats(exp): atol = 1e-6 if not exact_match and atol != 0: assert np.allclose(out, exp, rtol=1e-07, atol=atol) else: assert exact_match def check(candidate): inputs = [[[1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3], [[1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05], [[1.0, 2.0, 5.9, 4.0, 5.0], 0.95], [[1.0, 2.0, 5.9, 4.0, 5.0], 0.8], [[1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1], [[1.1, 2.2, 3.1, 4.1, 5.1], 1.0], [[1.1, 2.2, 3.1, 4.1, 5.1], 0.5], [[1.0, 2.5, 3.0, 4.5, 5.0, 6.5], 0.4], [[1.0, 2.0, 3.0, 2.9, 5.0, 6.0], 0.1], [[0.5, 0.6, 0.7, 0.8, 0.9], 0.05], [[10.5, 20.5, 30.5, 25.5, 40.5], 4.0], [[1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7], 0.2], [[0.1, 0.5, 1.0, 1.5, 2.0], 0.1], [[1.2, 2.4, 3.6, 4.8], 0.8], [[-1.0, -0.5, 0.0, 0.5, 1.0], 0.3], [[-2.0, 2.0, 3.0, 4.0], 1.5], [[1.0, 2.0, 3.0, 4.0, 5.0], 0.5], [[1.0, 2.0, 2.9, 5.0, 6.0], 4.4], [[1.0, 2.0, 3.0, 4.0, 5.0], 1.0], [[1.0, 2.0, 3.0, 4.0, 5.0, 4.0], 6.6], [[1.0, 2.5, 3.0, 4.5, 5.0, 6.5], 0.5837785211547324], [[1.0, 2.0, 3.0, 6.0], 1.4684959831900988], [[1.0, 2.0, 3.0, 4.0, 5.0, 4.0], 1.5], [[0.1, 0.5, 1.0, 1.5, 2.0], 5.0], [[1.0, 2.0, 3.0, 6.0], 0.5], [[1.0, 2.0, 3.0, 4.0, 5.0, 4.0, 3.1145064227603365], 1.5], [[1.0, 2.0, 3.0, 2.9, 5.0, 6.0, 6.6], 0.1], [[-2.0, 2.0, 3.0, 4.0], 1.7909669082553559], [[-1.0, -0.5, 0.0, 0.5, 1.0], 0.05], [[0.1, 0.5, 1.5, 2.0], 0.1], [[10.5, 20.5, 30.5, 25.5, 40.5, -1.0], 4.0], [[1.0, 1.4684959831900988, 3.0, 4.0, 5.0, 4.0], 6.6], [[1.0, 2.0, 3.0], 1.4684959831900988], [[1.0, 2.0, 3.0, 2.9, 5.0, 3.1145064227603365, 6.6], 0.001597775988153055], [[1.0, 2.0, 3.0, 4.0, 5.0, 4.0, 0.001597775988153055], 6.622770022559026], [[0.5, 0.7, 0.8, 0.9], 0.05], [[1.0, 2.0, 3.0, 4.0, 5.0], 1.4453152842907233], [[1.0, 2.5, 3.0, 4.5, 5.0, 6.5], 0.3], [[2.2, 3.3, 4.4, 5.5, 6.6, 7.7], 0.2], [[1.0, 3.0, 4.0, 5.0], 0.5], [[0.1, 0.5, 1.0, 1.5, 2.0, 0.5], 0.4], [[1.0, 2.0, 3.0, 2.9, 5.0, 6.0, 2.0, 2.0], 0.4395081641974091], [[1.0, 2.5, 3.0, 4.5, 6.5], 0.3], [[1.0, 2.0, 3.0, 4.0, 5.0, 2.4442803957785837, 1.0], 0.5], [[1.0, 3.0, 4.0, 5.0], 30.5], [[1.0, 2.5, 3.0, 4.5, 5.0, 5.40171670636793, 5.40171670636793], 0.3], [[0.5, 0.6, 0.7, 0.8], 3.1145064227603365], [[1.0, 2.0, 6.0], 1.4684959831900988], [[1.2, 2.4, 5.5, 4.8], 2.4], [[2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 5.5], 0.2], [[1.0, 2.0, 3.0, 6.0], 0.4], [[-2.0, 3.0, 4.0], 1.7909669082553559], [[0.1, 0.5, 1.0, 1.5, 2.0], 0.4], [[0.1, 0.5, 1.0, 1.5, 2.0, 6.0], 4.029509770611085], [[1.2, 2.4, 6.622770022559026, 4.8], 3.0791117034371744], [[1.0, 2.5, 3.0, 4.5, 5.0, 6.5, 5.51097214523066], 0.5837785211547324], [[0.1, 0.5, 1.0, 1.5, 2.0], 4.8], [[1.0, 2.0, 3.0, 2.9, 5.0, 3.1145064227603365, 6.6], 25.5], [[1.0, 2.0, 3.0, 2.9, 5.0, 6.0, 2.0, 2.0], 10.5], [[1.0, 3.0], 4.28359856787508], [[1.0, 2.0, 3.0, 2.9, 6.0], 0.1], [[1.2, 2.4, 6.32960401348287, 4.8], 2.4], [[-2.0, 4.029509770611085, 3.0, 4.0], 1.5], [[1.2, 2.4, 6.32960401348287, 4.8], 2.5], [[1.0, 2.0, 3.0, 2.9, 5.0, 6.0, 2.0], 0.1], [[1.2, 2.4, 6.32960401348287, 4.8], 0.1], [[1.2, 2.4, 6.32960401348287, 4.220732489926661], 1.5434921314453987], [[-1.0, -0.5, 0.0, 0.5, 1.0], 3.0791117034371744], [[2.0, 3.0], 0.4], [[0.1, 0.5, 1.0, 1.5, 2.0, 0.5], 0.5872031050795127], [[10.5, 20.5, 30.5, 25.5, 40.5], 4.08640576656449], [[0.5, 0.7, 0.8, 0.9], 0.5956038469909051], [[1.0, 2.0, 3.0, 4.0, 5.0, 4.0, 3.1145064227603365, 3.7502832308571223], 1.5], [[-2.0, 2.0, 3.0, 4.0], 6.622770022559026], [[10.5, 30.5, 25.5, 40.5], 4.08640576656449], [[0.1, 0.5, 1.0, 1.5, 2.0], 0.5872031050795127], [[1.0, 2.0, 3.0, 4.0, 5.0, 4.0, 0.001597775988153055, 4.08640576656449], 6.622770022559026], [[1.0, 2.0, 3.0, 6.0, 2.0], 0.4], [[1.2, 2.4, 6.32960401348287, 4.8, 2.4], 2.4], [[1.2, 2.4, 6.622770022559026, 4.8], 9.342174793939517], [[2.4, 6.32960401348287, 4.8], 2.4], [[1.2, 2.4, 6.622770022559026, 4.8], 7.7], [[1.0, 9.342174793939517, 4.0, 5.0], 30.5], [[1.0, 2.0, 3.0, 4.0, 5.0], 6.622770022559026], [[0.1, 0.5, 1.0, 1.5, 2.0, 2.7536386742063454], 0.5872031050795127], [[0.5, 0.6, 0.7, 0.8, 0.5956038469909051], 7.7], [[-1.0, 1.0, 0.0, 0.5, 1.0], 0.3], [[1.0, 2.0, 3.0, 2.9, 5.0, 6.0, 2.0, 2.0], 5.5], [[1.0, 2.0, 3.0, 2.9, 5.0, 6.0, 2.0, 2.0], 0.46346457679116604], [[1.0, 2.5, 3.0, 5.0, 6.5, 0.4395081641974091], 0.5837785211547324], [[2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 5.5], 3.3], [[-2.0, 2.0, 3.0, 4.0], 0.05], [[2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 5.5], 0.22976043999045262], [[1.2, 2.4, 6.622770022559026, 4.8], 1.5434921314453987], [[1.2, 2.4, 6.622770022559026, 4.8], 1.6376315868318712], [[2.2, 3.3, 4.4, 5.5, 7.7, 5.5], 0.22976043999045262], [[1.0, 0.0, 0.5, 1.0], 0.3], [[1.2, 2.4, 6.32960401348287, 4.8], 0.22976043999045262], [[1.2, 2.4, 6.32960401348287, 4.8, 2.4], 0.22976043999045262], [[1.2, 2.4, 3.6, 4.8, 1.2], 0.8], [[1.0, 2.0, 3.0, 2.9, 5.0, 6.0], 1.2], [[1.0, 2.0, 3.0, 4.0, 5.0, 4.0, 3.1145064227603365, 3.7502832308571223, 4.814818917148268], 1.5], [[1.2, 2.4, 6.32960401348287, 4.8], 1.8783433029023973], [[1.2, 2.4, 6.32960401348287, 4.8, 4.8, 1.6616978677034633], 0.09078602525716209], [[1.0, 2.5, 3.0, 3.907675941633249, 5.0, 6.5, 4.5], 0.6417930585021959], [[1.0, 2.0, 3.0, 2.9, 5.0, 6.0, 2.518151204418766, 2.0], 2.0], [[-2.0, 4.029509770611085, 3.0, 4.0], 25.5], [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], 1.0], [[0.0001, 0.0002, 0.0003, 0.0004, 0.0005], 1e-05], [[0.01, 0.02, 0.03, 0.04, 0.05], 0.001], [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], 0.1], [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], 1e-07], [[0.0, -1.0, 1.0, 2.0, 3.0, 4.0, 27.0, -3.5, 3.5], 0.5], [[0.5, 1.5, 2.5, 3.5, 4.5], 0.25], [[2.0, 4.0, 6.0, 8.0, 10.0], 0.4], [[0.01, 0.02, 0.03, 0.04, 0.05, 0.06], 0.001], [[1.0, 2.0, 2.9, 3.0, 3.1, 4.0, 5.0, 6.0], 0.2], [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 1.0], 1.0], [[0.0, -1.0, 1.0, 2.0, 3.0, 4.0, 27.0, -3.5, 3.5], 0.0002], [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], 0.01], [[2.0, 4.0, 6.0, 0.4, 10.0], 0.3534420658062394], [[1.0, 0.02, 3.0, 4.0, 5.0, 6.0, 7.0, 0.02], 0.01], [[0.5, 1.5, 2.5, 3.5, 4.5], 1e-05], [[0.01, 2.9, 0.03, 0.04, 0.05, 0.06], 0.001], [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], 1.1411672170968274], [[2.0, 4.0, 6.0, 8.0, 10.0], 0.02], [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], 0.0002], [[0.5, -1.0, 1.0, 2.0, 3.0, 4.0, 27.0, -3.5, 3.5], 0.7151289158513581], [[2.0, 4.0, 6.0, 8.0, 10.0], 4.5], [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 1.0], 0.5876482754436165], [[0.01, 2.9, 0.03, 0.04, 0.05, 0.06, 0.01], 0.001], [[0.01, 1e-07, 0.03, 0.04, 0.05, 0.06], 0.03], [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], 0.05], [[0.01, 0.03, 0.04, 0.06], 0.001], [[0.01, 0.02, 0.03, 0.04, 0.05], 0.0009536407568491921], [[0.0001, 0.0002, 0.0003, 0.0004, 0.0005], 3.0], [[0.5, 1.5, 2.5, 3.5, 4.5], 10.0], [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 3.0], 0.0002], [[1.0, 2.0, 3.0, 4.0, 0.05, 5.0, 6.0, 7.0], 1.1411672170968274], [[0.01, 0.02, 0.03, 0.03791583967678999, 0.05, 0.06], 0.001], [[0.0, -1.0, 1.0, 2.0, 3.0, 4.0, 27.0, -3.5, 3.5], 0.5815567161870863], [[0.01, 2.9, 2.419821239840023, 0.03, 0.04, 0.05, 0.06], 0.001], [[0.01, 0.02, 0.03, 0.03791583967678999, 0.06], 0.001], [[0.0001, 0.0002, 0.0003, 0.0004, 0.0005], 3.496608712535931], [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 0.001, 7.0], 1e-07], [[1.0, 2.0, 3.0, 4.0, 0.05, 5.0, 6.0, 7.0], 0.01], [[0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0001], 3.0], [[0.0001, 0.0002, 0.0003, -0.27533868871906764, 0.0004, 0.0005], 3.0], [[5.0, 0.01, 0.02, 0.03, 0.03791583967678999, 0.05, 0.06], 0.001], [[0.01, 0.02, 0.03, 2.419821239840023, 0.04, 0.05], 0.4], [[1.0, 2.0, 2.9, 3.0, 3.1, 4.0, 5.0, 6.0], 0.7065364188537329], [[5.0, 0.01, 0.02, 0.03, 0.05, 0.06], 0.001], [[2.0, 4.0, 6.0, 5.4033516333681195, 0.4], 0.3534420658062394], [[0.0001, 0.0002, 0.0003, 0.0004, 0.0005], 0.03791583967678999], [[1.0, 2.0, 2.9, 3.0, 3.1, 4.0, 5.0, 6.0], 0.23476792155713733], [[0.01, 0.0005, 0.03, 0.04, 0.06], 0.001], [[2.0, 4.0, 6.0, 10.0], 0.02], [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 1.0], 1.4554927325266915], [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], 0.5387982733401486], [[0.01, 0.02, 0.03, 0.03791583967678999, 0.06, 0.06], 0.001], [[1.0, 2.4342970911810413, 3.0, 4.0, 0.05, 5.0, 6.0, 7.0], 1.1411672170968274], [[1.0, 0.01, 2.9, 3.0, 3.1, 4.0, 5.0, 6.0], 0.2], [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], 0.03], [[1.0, 0.01, 2.9, 3.0, 5.582758456545748, 3.1, 4.0, 5.0, 6.0], 0.2], [[0.01, 2.9, 2.419821239840023, 0.03, 0.04, 0.04634346891165053, 0.06], 0.001], [[0.0, -1.0, 1.0, 2.0, 3.0, 4.0, 27.0, -3.5, 3.5, -1.0], 5.582758456545748], [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], 27.0], [[0.01, 0.0005, 0.03, 0.04, 0.06], 3.5], [[2.0, 4.0, 6.0, 8.0, 5.403976711699473, 10.0], 0.02], [[1.0, 2.419821239840023, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], 1.1411672170968274], [[1.0, 0.01, 3.0, 3.1, 0.23476792155713733, 4.0, 5.0, 6.0], 0.24986270391526247], [[0.01, 0.02, 0.03, 2.419821239840023, 0.04, 0.05, 0.05], 5.0], [[0.01, 0.03, 0.05, 0.06], 2.419821239840023], [[0.01, 0.02, 0.03, 0.03791583967678999, 0.05, 0.06, 0.01], 0.001], [[2.0, 4.0, 6.0, 8.0, 5.403976711699473, 10.0], 10.0], [[2.0, 4.0, 6.0, 5.4033516333681195, 0.4], 0.0004], [[0.01, 0.03, 0.04, 0.06, 0.01], 0.001], [[1.0, 2.0, 3.0, 4.0, 0.0005, 6.0, 7.0], 1.1411672170968274], [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0], 1e-07], [[0.01, 0.0005, 0.03, 0.04, 0.06], 1.4554927325266915], [[0.0001, 0.0002, 0.0003, -0.006819962504528854, 0.0004, 0.0005], 5.0], [[0.01, 0.02, 0.03, 0.03791583967678999, 0.05, 0.06, 0.01, 0.06], 0.001], [[0.0001, 0.0002, 0.0003, 0.0004, 0.0005], 4.922894421556738], [[0.0, -1.0, 1.0, 2.0, 3.0, 4.0, 27.0, -3.5, 3.5, 3.0], 0.5], [[0.01, 0.02, 0.03, 0.05, 0.06, 0.01], 0.8868666223424293], [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0], 4.5], [[1.0, 2.0, 3.0, 4.0, 0.05, 5.0, 6.0, 7.0], 0.02], [[2.0, 8.0, 6.0, 8.0, 10.0], 0.04634346891165053], [[1.0, 0.02, 3.0, 5.0, 6.0, 7.0, 0.02], 0.01], [[0.01, 0.0005, 0.03, 0.04, 0.06], 10.0], [[0.0, -1.0, 1.0, 2.0, 3.0, 4.0, 27.0, -3.5, 3.5, 27.0], 0.5], [[5.0, 0.01, 0.02, 0.03, 0.05, 0.06], 3.0], [[0.0001, 0.0002, 0.0003, -0.27533868871906764, 0.0009536407568491921, 0.0004, 0.0005], 3.0], [[0.01, 0.02, -0.27533868871906764, 0.04, 0.05], 0.0009536407568491921], [[0.0001, 0.0002, 0.0003, -0.006819962504528854, 0.0004, 0.0005], 7.2430807150773235], [[0.0, -1.0, 1.0, 2.0, 3.0, 4.0, 27.0, -3.5, 3.5, -1.0], 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3.4789370733342704], [[0.0, -1.0, 1.0, 2.0, 0.5, 3.0, 4.0, 27.0, -3.5, 3.5], 0.9164049075273353], [[0.01, 0.02, 0.0, 3.1, 0.04, 4.320267231190421], 0.4489610242046808], [[2.9, 0.0002, 0.0004], 0.06551718965331986], [[0.0001, 0.0002, 0.0003, 0.0004, 3.0, 0.0003], 5.932113294381724], [[0.027058224311019348, 0.0001, 0.0002, 0.0003, 4.320267231190421, 0.0004, 0.0005, 0.0005, 0.0004], 0.05], [[0.0001, 0.0002, 0.0003, 0.0004, 0.9236125030833611, 0.0005, 0.0005], 0.34173466739005054], [[0.01, 0.03, 0.04, 0.05, 0.06], 1.9255680498201777], [[1.0, 2.0, 3.0, 4.0, 5.0, 3.1, 6.0, 7.0], 0.7923707008984809], [[0.01, 0.03, 0.04, 0.05, 0.06, 0.04, 0.01], 0.24915745094654063], [[1.0, 2.0, 3.0, 4.0, 5.0, 7.0, 4.831203365732389, 1.0], 0.1], [[0.01, 0.03, 0.04, 0.05], 0.7083071514757247], [[0.0, -1.0, 1.0, 2.0, 3.0, 4.0, 27.0, -3.5, 3.5], 0.6732530490080072], [[5.6316080215521163e-05, 0.0002, 0.0003, 10.360620825036374, 0.0004, 0.0005, 2.5, 0.0003, 10.360620825036374], 0.000864368171540714], [[0.0, -1.0, 1.0, 2.0, 3.0, 4.0, 27.0, -3.5, 3.5, 4.0], 3.80230126436164], [[0.0001, 0.0002, 0.0003, 0.0004, 0.0005], 0.2366548975305903], [[0.01, 0.02, 0.03, 0.04, 0.05, 0.06], 3.80230126436164], [[2.546056560331472, 1.5, 2.5, 3.5, 4.5], 0.9748345950231323], [[0.0001, 4.0, 0.0004, 0.0005, 0.0005], 1.258526307128289e-05], [[0.5, 1.5, 0.0004849742100830799, 2.5, 3.5, 4.5], 0.05766460375946958], [[1.0, 2.0, 3.0, 4.0, 4.369218129512683, 6.0, 7.0, 4.0, 0.7690733466089933, 3.0], 10.360620825036374], [[1.0, 3.4789370733342704, 2.0, 3.0, 4.0, 5.0, 3.1, 6.0, 7.0], 0.4721319649371413], [[4.554151651658612, 0.02, 0.03, 0.04, 0.05], 3.307663458004549], [[0.0, -1.0, 1.0, 2.0, 3.0, 4.0, 27.0, -3.5, 3.5], 0.5803213720025598], [[0.0, -1.0, 1.0, 2.0, 3.0, 4.0, 27.0, -3.5, 3.5], 3.75736209595818], [[0.5, 0.7830519816499151, 1.5, 2.5, 3.5, 4.5], 2.9252594886571455], [[1.0, 2.0, 3.0, 4.0, 5.0, 7.0, 4.0, 3.0, 3.0, 3.0, 4.0], 4.35968221414135], [[2.0, 3.0, 0.2366548975305903, 5.932113294381724, 7.0, 5.932113294381724], 3.6353279062810095], [[0.0001, 0.0003, 0.0004, 0.0005, 0.0005, 0.0001, 0.0003, 0.0003, 0.0003], 1.4084871604544225e-05], [[0.0001, 0.0002, -0.49573144003113767, 0.0003, 0.0004], 0.008289598692317044], [[0.01, 0.02, 0.03, 0.04, 3.0036344781111617, 0.05, -0.15838574353129323, 0.06], 2.8139825387707576e-08], [[6.0, 1.0, 2.0, 3.0, 5.0, 6.0], 8.427615438201482], [[0.0001, 0.0002, 0.0003, 0.9236125030833611, 0.0005], 0.10215110330888037]] results = [True, False, True, False, True, True, False, False, False, False, False, False, False, False, False, True, False, True, False, True, True, True, True, True, False, True, False, True, False, False, False, True, True, False, True, False, True, False, False, False, True, True, False, True, True, True, True, True, True, True, False, True, False, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, False, True, False, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False, True, True, False, False, False, True, False, False, True, False, False, True, True, True, True, True, False, False, False, True, True, True, True, False, True, False, False, True, False, False, True, True, False, True, True, False, False, True, True, False, False, True, False, True, True, True, False, True, False, True, True, True, False, True, True, True, True, True, True, False, True, True, False, True, True, True, True, True, True, True, False, True, True, True, True, True, True, False, True, True, True, True, False, True, False, True, True, True, True, True, True, False, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, False, False, True, True, True, True, True, True, True, False, False, True, True, True, True, True, True, True, True, True, False, False, True, True, False, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, False, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, False, False, True, False, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, False, False, True, False, True, True, False, True, True, True, True, True, True, True, True, True, True, False, True, False, True, True, False, True, True, True, True, False, True, True, False, False, True, False, True, True, True, True, True, True, False, True, True, False, False, True, True, False, False, False, False, True, True, True, True, True, True, True, True, True, True, True, True, False, False, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, False, False, False, True, True, True, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, False, True, False, True, True, False, True, False, True, True, True, True, True, True, True, True, True, True, True, False, True, False, False, True, True, True, True, True, True, False, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, False, True, True, True, False, True, True, True, False, True, True, True, False, True, True, True, True, False, True, False, False, True, True, True, True, True, True, True, False, True, False, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, False, False, True, True, False, False, True, True, True, False, True, True, True, True, False, True, False, False, True, False, True, True, False, False, True, True, True, False, True, False, True, True, True, True, True, True, True, True, False, True, True, True, False, True, False, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, False, True, True, True, True, True, False, True, True, True, True, True, True, True, True, False, True, True, True, True, False, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, False, False, False, True, True, False, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, False, True, True, True, True, False, True, True, True, True, True, True, False, True, True, True, True, False, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, False, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, False, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, False, True, True, True, True, True, True, False, True, True, True, False, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, False, True, True] for i, (inp, exp) in enumerate(zip(inputs, results)): assertion(candidate(*inp), exp, 0) check(has_close_elements)	Check if in given list of numbers, are any two numbers closer to each other than given threshold. >>> has_close_elements([1.0, 2.0, 3.0], 0.5) False >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3) True	from typing import List def has_close_elements(numbers: List[float], threshold: float) -> bool:
HumanEval/1	from typing import List def separate_paren_groups(paren_string: str) -> List[str]: """ Input to this function is a string containing multiple groups of nested parentheses. Your goal is to separate those group into separate strings and return the list of those. Separate groups are balanced (each open brace is properly closed) and not nested within each other Ignore any spaces in the input string. >>> separate_paren_groups('( ) (( )) (( )( ))') ['()', '(())', '(()())'] """	cnt, group, results = 0, "", [] for ch in paren_string: if ch == "(": cnt += 1 if ch == ")": cnt -= 1 if ch != " ": group += ch if cnt == 0: if group != "": results.append(group) group = "" return results	separate_paren_groups	import numpy as np def is_floats(x) -> bool: # check if it is float; List[float]; Tuple[float] if isinstance(x, float): return True if isinstance(x, (list, tuple)): return all(isinstance(i, float) for i in x) if isinstance(x, np.ndarray): return x.dtype == np.float64 or x.dtype == np.float32 return False def assertion(out, exp, atol): exact_match = out == exp if atol == 0 and is_floats(exp): atol = 1e-6 if not exact_match and atol != 0: assert np.allclose(out, exp, rtol=1e-07, atol=atol) else: assert exact_match def check(candidate): inputs = [['(()()) ((())) () ((())()())'], ['() (()) ((())) (((())))'], ['(()(())((())))'], ['( ) (( )) (( )( ))'], ['()'], ['(())'], ['((()))()'], ['()()()'], ['()(()())()'], ['(((()()((())))))'], ['((()()))(())'], ['()(()()((())))'], ['()()()(((()())))()(())'], ['(((())))()((()()))'], ['(((())))()(((())))()((()()))((()()))'], ['((((((())))()(((())))()((()()))((()())))()))(())'], ['()(()())()(()()((())))()'], ['()(()()((()))(((()()((()))))))'], ['()()((((()()((()))))))'], ['()()((((()()((()))))()()()))'], ['()()()(((()())))()((()))'], ['((()()((((((())))()(((())))()((()()))((()())))()))(())))(())'], ['()()()(()()()(((()())))()((()))((()())))()(())'], ['()()(()())()(()())()'], ['(((()))())()((()()))'], ['()()(()())()(()()((()))())()'], ['()()((((()()((()))))))()(()())()(()()((())))()'], ['()()(((((()()((()))))))()(()())()(()))()'], ['((() ((()()(((((( )))))))()()))) (()(()))()(()())'], ['((( ) ((( )) () (( ))))) (((( )))) (( ))'], ['(()) (()) ((() ((())))()) ((())(()))()'], ['((()) ()) ()(()) (()(())())'], ['((()) (())() (())(()))()(((())))((()()))((()))'], ['(((())))(((())))(((())))(((())))(((())))(((())))(((())))(((())))(((())))(((())))'], ['(((())))(((())))(((())))(((())))(((())))(((())))(((())))(((())))(((())))(((())))()()()()()'], ['()()()()()()((()))()()()((()))()()()((()))()()()((()))()()()((()))()()()((()))()()()((()))()()()'], ['() ( ) (( )) ((( )))()(()()()) ((()())())() (()()()()()())'], ['((( ) (((()) (()) ((() ((())))()) ((())(()))()( )) () (( ))))) (((( )))) (( ))'], ['(()()()()()())'], ['(()) (()) '], ['((( ) (((())) (()) ((() ((())((((((() ((() ((() ())))))))))))()) ((())(()))()( )) () (( ))))) (((( )))) (( ))'], ['(()) ((((() ((()()(((((( )))))))()()))) (()(()))()(()()))) '], ['((( ) (((()(())())) (()) ((() ((())((((((() ((() ((() ())))))))))))()) ((())(()))()( )) () (( ))))) (((( )))) (( ))'], ['()(())'], ['(())()'], ['(()(()))'], ['((( ) (((()(())()) (()) ((() ((())((((((() ((() ((() ())))))))))))()) ((())(()))()( )) (())(()))()((((()))((())))((()()))((())))() (( ))))) (((( )))) (( ))'], ['(())(())'], [' (()) (()) ((() ((())))()) ((())(()))()'], ['((())(()))()'], ['()((()()()()()()((()))()()()((()))()()()((()))()()()((()))()()()((()))()()()((()))()()()((()))()()()))'], ['(()(())())'], [''], ['((( ) )(((())) (()(())(( ))))) (((( )))) (( ))'], ['((()) ()) ()(( )) (()(())())'], ['(()()()())'], ['()(()())()(())'], ['()()()()()()((()))()()()((()))()()(())'], ['(()(()))()(()())'], ['((()())())()'], ['((( ) (((()) (()) ((() ((()))))()) (()()()()()())((())(()))()( )) () (( ())))) (((( )))) (( ))'], ['((( )) ()) ()(()) (()(())())'], ['((((((()) ((() ((() ())))))))))'], ['(((()) ()) ())'], ['(())((()(((())))((()()))(((((()) (())() (())(())((())((( ) (((()) (()) ((() ((())))()) (()()()()()())((())(()))()( )) () (( ())))) (((( )))) (( )))))()))((())))))'], ['( ) (( )) (( )( )) ()()()'], ['(())(()())()((((((()))))))'], ['( )'], ['((()))'], ['((( ) ((( )) () (( ))))) (((( )))) (( ))'], ['((( ) ((( )((( ) ((( )) () (( ))))) (((( )))) (( ))) () (( ))))) (((( )))) (( ))'], ['() ( ) (( )) ((( )))()(()()()) ((()())())()(()()()()()())'], ['(((())))(((())))(((())))(((())))(((())))(((())))(((())))(((())()(())))(((())))(((())))()()()()()'], ['((()())())()(()()()()()())'], ['((()))(()()())'], ['((( ))) (( ))'], ['(((())))'], ['((() ((()()))))()(()()())()(()())'], ['(((((((() (((()()))))))))))'], ['((()))()(())'], ['()()()()()()((()))()()()((()))()()()((()))()()()((()))()()()'], ['(()())()'], ['((())(()(()(()))()(()())()()((()))()()()((()))()))()'], ['((( ) ((( )) () (( ))))) (((( )) )) (( ))'], ['((()())())()(()(((()())()(()()))()(()())()()((())))()()()((())())()()()())'], ['(((()())))'], ['(())()()()((()))()()()']] results = [['(()())', '((()))', '()', '((())()())'], ['()', '(())', '((()))', '(((())))'], ['(()(())((())))'], ['()', '(())', '(()())'], ['()'], ['(())'], ['((()))', '()'], ['()', '()', '()'], ['()', '(()())', '()'], ['(((()()((())))))'], ['((()()))', '(())'], ['()', '(()()((())))'], ['()', '()', '()', '(((()())))', '()', '(())'], ['(((())))', '()', '((()()))'], ['(((())))', '()', '(((())))', '()', '((()()))', '((()()))'], ['((((((())))()(((())))()((()()))((()())))()))', '(())'], ['()', '(()())', '()', '(()()((())))', '()'], ['()', '(()()((()))(((()()((()))))))'], ['()', '()', '((((()()((()))))))'], ['()', '()', '((((()()((()))))()()()))'], ['()', '()', '()', '(((()())))', '()', '((()))'], ['((()()((((((())))()(((())))()((()()))((()())))()))(())))', '(())'], ['()', '()', '()', '(()()()(((()())))()((()))((()())))', '()', '(())'], ['()', '()', '(()())', '()', '(()())', '()'], ['(((()))())', '()', '((()()))'], ['()', '()', '(()())', '()', '(()()((()))())', '()'], ['()', '()', '((((()()((()))))))', '()', '(()())', '()', '(()()((())))', '()'], ['()', '()', '(((((()()((()))))))()(()())()(()))', '()'], ['((()((()()(((((()))))))()())))', '(()(()))', '()', '(()())'], ['((()((())()(()))))', '(((())))', '(())'], ['(())', '(())', '((()((())))())', '((())(()))', '()'], ['((())())', '()', '(())', '(()(())())'], ['((())(())()(())(()))', '()', '(((())))', '((()()))', '((()))'], ['(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '(((())))'], ['(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '()', '()', '()', '()', '()'], ['()', '()', '()', '()', '()', '()', '((()))', '()', '()', '()', '((()))', '()', '()', '()', '((()))', '()', '()', '()', '((()))', '()', '()', '()', '((()))', '()', '()', '()', '((()))', '()', '()', '()', '((()))', '()', '()', '()'], ['()', '()', '(())', '((()))', '()', '(()()())', '((()())())', '()', '(()()()()()())'], ['((()(((())(())((()((())))())((())(()))()())()(()))))', '(((())))', '(())'], ['(()()()()()())'], ['(())', '(())'], ['((()(((()))(())((()((())((((((()((()((()())))))))))))())((())(()))()())()(()))))', '(((())))', '(())'], ['(())', '((((()((()()(((((()))))))()())))(()(()))()(()())))'], ['((()(((()(())()))(())((()((())((((((()((()((()())))))))))))())((())(()))()())()(()))))', '(((())))', '(())'], ['()', '(())'], ['(())', '()'], ['(()(()))'], ['((()(((()(())())(())((()((())((((((()((()((()())))))))))))())((())(()))()())(())(()))()((((()))((())))((()()))((())))()(()))))', '(((())))', '(())'], ['(())', '(())'], ['(())', '(())', '((()((())))())', '((())(()))', '()'], ['((())(()))', '()'], ['()', '((()()()()()()((()))()()()((()))()()()((()))()()()((()))()()()((()))()()()((()))()()()((()))()()()))'], ['(()(())())'], [], ['((())(((()))(()(())(()))))', '(((())))', '(())'], ['((())())', '()', '(())', '(()(())())'], ['(()()()())'], ['()', '(()())', '()', '(())'], ['()', '()', '()', '()', '()', '()', '((()))', '()', '()', '()', '((()))', '()', '()', '(())'], ['(()(()))', '()', '(()())'], ['((()())())', '()'], ['((()(((())(())((()((()))))())(()()()()()())((())(()))()())()((()))))', '(((())))', '(())'], ['((())())', '()', '(())', '(()(())())'], ['((((((())((()((()())))))))))'], 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'()'], ['(()())', '()'], ['((())(()(()(()))()(()())()()((()))()()()((()))()))', '()'], ['((()((())()(()))))', '(((())))', '(())'], ['((()())())', '()', '(()(((()())()(()()))()(()())()()((())))()()()((())())()()()())'], ['(((()())))'], ['(())', '()', '()', '()', '((()))', '()', '()', '()']] for i, (inp, exp) in enumerate(zip(inputs, results)): assertion(candidate(*inp), exp, 0) check(separate_paren_groups)	Input to this function is a string containing multiple groups of nested parentheses. Your goal is to separate those group into separate strings and return the list of those. Separate groups are balanced (each open brace is properly closed) and not nested within each other Ignore any spaces in the input string. >>> separate_paren_groups('( ) (( )) (( )( ))') ['()', '(())', '(()())']	from typing import List def separate_paren_groups(paren_string: str) -> List[str]:
HumanEval/2	def truncate_number(number: float) -> float: """ Given a positive floating point number, it can be decomposed into and integer part (largest integer smaller than given number) and decimals (leftover part always smaller than 1). Return the decimal part of the number. >>> truncate_number(3.5) 0.5 """	return number - int(number)	truncate_number	import numpy as np def is_floats(x) -> bool: # check if it is float; List[float]; Tuple[float] if isinstance(x, float): return True if isinstance(x, (list, tuple)): return all(isinstance(i, float) for i in x) if isinstance(x, np.ndarray): return x.dtype == np.float64 or x.dtype == np.float32 return False def assertion(out, exp, atol): exact_match = out == exp if atol == 0 and is_floats(exp): atol = 1e-6 if not exact_match and atol != 0: assert np.allclose(out, exp, rtol=1e-07, atol=atol) else: assert exact_match def check(candidate): inputs = [[3.5], [1.33], [123.456], [999.99999], [0.3333333], [1.0], [1.5], [0.5], [0.999999], [500.00678], [11.11], [0.25], [5.99], [1.284163165748358], [1.8517611088029418], [0.7713842427275083], [3.1842364304976463], [1.0738831860229472], [0.04320870526393539], [1.1369261836552624], [1.0630407193724771], [10.291122396192739], 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0.9379060141393113, 0.018399968753522344, 0.36196762181635134, 0.2149144302339392, 0.02931322313087814, 2.4602748862251587e-14, 0.6545550268805593, 0.6600822358584795, 0.8813790109677413, 0.8692630331945841, 0.42558860778808594, 0.9140265882015228, 7.122831784956886e-14, 0.9987209717224221, 0.3818931579589844, 0.5815410614013672, 0.9895403385162354, 0.995693783949557, 0.9909011928781961, 0.5246288478374481, 0.9344451258617301, 0.6848623752593994] for i, (inp, exp) in enumerate(zip(inputs, results)): assertion(candidate(*inp), exp, 1e-06) check(truncate_number)	Given a positive floating point number, it can be decomposed into and integer part (largest integer smaller than given number) and decimals (leftover part always smaller than 1). Return the decimal part of the number. >>> truncate_number(3.5) 0.5	def truncate_number(number: float) -> float:
HumanEval/3	"from typing import List\n\n\ndef below_zero(operations: List[int]) -> bool:\n \"\"\" You're give(...TRUNCATED)	"\n account = 0\n for operation in operations:\n account += operation\n if accou(...TRUNCATED)	below_zero	"\n\nimport numpy as np\n\ndef is_floats(x) -> bool:\n # check if it is float; List[float]; Tuple(...TRUNCATED)	"You're given a list of deposit and withdrawal operations on a bank account that starts with\nzero b(...TRUNCATED)	from typing import List def below_zero(operations: List[int]) -> bool:
HumanEval/4	"from typing import List\n\n\ndef mean_absolute_deviation(numbers: List[float]) -> float:\n \"\"\(...TRUNCATED)	"\n mean = sum(numbers) / len(numbers)\n return sum(abs(x - mean) for x in numbers) / len(numb(...TRUNCATED)	mean_absolute_deviation	"\n\nimport numpy as np\n\ndef is_floats(x) -> bool:\n # check if it is float; List[float]; Tuple(...TRUNCATED)	"For a given list of input numbers, calculate Mean Absolute Deviation\naround the mean of this datas(...TRUNCATED)	from typing import List def mean_absolute_deviation(numbers: List[float]) -> float:
HumanEval/5	"from typing import List\n\n\ndef intersperse(numbers: List[int], delimeter: int) -> List[int]:\n (...TRUNCATED)	"\n\n res = []\n for i in range(len(numbers)):\n res.append(numbers[i])\n if i !(...TRUNCATED)	intersperse	"\n\nimport numpy as np\n\ndef is_floats(x) -> bool:\n # check if it is float; List[float]; Tuple(...TRUNCATED)	"Insert a number 'delimeter' between every two consecutive elements of input list `numbers'\n>>> int(...TRUNCATED)	from typing import List def intersperse(numbers: List[int], delimeter: int) -> List[int]:
HumanEval/6	"from typing import List\n\n\ndef parse_nested_parens(paren_string: str) -> List[int]:\n \"\"\" I(...TRUNCATED)	"\n \n def count_depth(s: str) -> int:\n max_depth, cnt = 0, 0\n for ch in s:\n (...TRUNCATED)	parse_nested_parens	"\n\nimport numpy as np\n\ndef is_floats(x) -> bool:\n # check if it is float; List[float]; Tuple(...TRUNCATED)	"Input to this function is a string represented multiple groups for nested parentheses separated by (...TRUNCATED)	from typing import List def parse_nested_parens(paren_string: str) -> List[int]:
HumanEval/7	"from typing import List\n\n\ndef filter_by_substring(strings: List[str], substring: str) -> List[st(...TRUNCATED)	return list(filter(lambda s: substring in s, strings))	filter_by_substring	"\n\nimport numpy as np\n\ndef is_floats(x) -> bool:\n # check if it is float; List[float]; Tuple(...TRUNCATED)	"Filter an input list of strings only for ones that contain given substring\n>>> filter_by_substring(...TRUNCATED)	"from typing import List\n\n\ndef filter_by_substring(strings: List[str], substring: str) -> List[st(...TRUNCATED)
HumanEval/8	"from typing import List, Tuple\n\n\ndef sum_product(numbers: List[int]) -> Tuple[int, int]:\n \"(...TRUNCATED)	"\n s, p = 0, 1\n for number in numbers:\n s += number\n p *= number\n return(...TRUNCATED)	sum_product	"\n\nimport numpy as np\n\ndef is_floats(x) -> bool:\n # check if it is float; List[float]; Tuple(...TRUNCATED)	"For a given list of integers, return a tuple consisting of a sum and a product of all the integers (...TRUNCATED)	from typing import List, Tuple def sum_product(numbers: List[int]) -> Tuple[int, int]:
HumanEval/9	"from typing import List, Tuple\n\n\ndef rolling_max(numbers: List[int]) -> List[int]:\n \"\"\" F(...TRUNCATED)	return [max(numbers[:(i+1)]) for i in range(len(numbers))]	rolling_max	"\n\nimport numpy as np\n\ndef is_floats(x) -> bool:\n # check if it is float; List[float]; Tuple(...TRUNCATED)	"From a given list of integers, generate a list of rolling maximum element found until given moment\(...TRUNCATED)	from typing import List, Tuple def rolling_max(numbers: List[int]) -> List[int]:

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