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B_human_eval_plus_v2

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train · 328 rows

test stringlengths 810 502k	hash stringlengths 32 32
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], 0.0002], [[0.0001, 0.0003, -0.006819962504528854, 0.0004, 0.0005], 7.2430807150773235], [[1.0, 2.0, 3.0, 4.0, 5.201263047726941, 6.0, 2.0], 6.228903746346452], [[5.0, 0.01, 0.02, 0.03, 0.03791583967678999, 0.05, 0.06], 0.0005], [[1.0, 0.02, 3.0, 5.0, 6.0, 7.0, 0.02], 1e-07], [[2.0, 4.0, 6.0, 5.403976711699473, 10.0], 0.02], [[0.0001, 0.0002, 0.0003, 0.0004, 0.0005], 4.054570532215202], [[2.0, 4.0, 6.0, 8.0, 10.0, 4.0], 4.553455122105357], [[0.0, -1.0, 1.0, 2.0, 3.0, 4.0, 27.0, -3.5, 3.5], 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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) print('___SENTINEL___')	338087b7a07338dadb5fa759bf391134
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-4 if not exact_match and atol != 0: assert np.allclose(out, exp, rtol=1e-04, atol=atol) else: assert exact_match def check(candidate): inputs = [[[1.0, 2.0, 3.0], 0.5], [[1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3]] results = [False, True] for i, (inp, exp) in enumerate(zip(inputs, results)): assertion(candidate(*inp), exp, 0) check(has_close_elements) print('___SENTINEL___')	3c27665677afdc42f48b33543c1a714f
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 = [['(()())', '((()))', '()', '((())()())'], ['()', '(())', '((()))', '(((())))'], ['(()(())((())))'], ['()', '(())', '(()())'], ['()'], ['(())'], ['((()))', '()'], ['()', '()', '()'], ['()', '(()())', '()'], ['(((()()((())))))'], ['((()()))', '(())'], ['()', '(()()((())))'], ['()', '()', '()', '(((()())))', '()', '(())'], ['(((())))', '()', '((()()))'], ['(((())))', '()', '(((())))', '()', '((()()))', '((()()))'], ['((((((())))()(((())))()((()()))((()())))()))', '(())'], ['()', '(()())', '()', '(()()((())))', '()'], ['()', '(()()((()))(((()()((()))))))'], ['()', '()', '((((()()((()))))))'], ['()', '()', '((((()()((()))))()()()))'], ['()', '()', '()', '(((()())))', '()', '((()))'], ['((()()((((((())))()(((())))()((()()))((()())))()))(())))', '(())'], ['()', '()', '()', '(()()()(((()())))()((()))((()())))', '()', '(())'], ['()', '()', '(()())', '()', '(()())', '()'], ['(((()))())', '()', '((()()))'], ['()', '()', '(()())', '()', '(()()((()))())', '()'], ['()', '()', '((((()()((()))))))', '()', '(()())', '()', '(()()((())))', '()'], ['()', '()', '(((((()()((()))))))()(()())()(()))', '()'], ['((()((()()(((((()))))))()())))', '(()(()))', '()', '(()())'], ['((()((())()(()))))', '(((())))', '(())'], ['(())', '(())', '((()((())))())', '((())(()))', '()'], ['((())())', '()', '(())', '(()(())())'], ['((())(())()(())(()))', '()', '(((())))', '((()()))', '((()))'], ['(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '(((())))'], ['(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '()', '()', '()', '()', '()'], ['()', '()', '()', '()', '()', '()', '((()))', '()', '()', '()', '((()))', '()', '()', '()', '((()))', '()', '()', '()', '((()))', '()', '()', '()', '((()))', '()', '()', '()', '((()))', '()', '()', '()', '((()))', '()', '()', '()'], ['()', '()', '(())', '((()))', '()', '(()()())', '((()())())', '()', '(()()()()()())'], ['((()(((())(())((()((())))())((())(()))()())()(()))))', '(((())))', '(())'], ['(()()()()()())'], ['(())', '(())'], ['((()(((()))(())((()((())((((((()((()((()())))))))))))())((())(()))()())()(()))))', '(((())))', '(())'], ['(())', '((((()((()()(((((()))))))()())))(()(()))()(()())))'], ['((()(((()(())()))(())((()((())((((((()((()((()())))))))))))())((())(()))()())()(()))))', '(((())))', '(())'], ['()', '(())'], ['(())', '()'], ['(()(()))'], ['((()(((()(())())(())((()((())((((((()((()((()())))))))))))())((())(()))()())(())(()))()((((()))((())))((()()))((())))()(()))))', '(((())))', '(())'], ['(())', '(())'], ['(())', '(())', '((()((())))())', '((())(()))', '()'], ['((())(()))', '()'], ['()', '((()()()()()()((()))()()()((()))()()()((()))()()()((()))()()()((()))()()()((()))()()()((()))()()()))'], ['(()(())())'], [], ['((())(((()))(()(())(()))))', '(((())))', '(())'], ['((())())', '()', '(())', '(()(())())'], ['(()()()())'], ['()', '(()())', '()', '(())'], ['()', '()', '()', '()', '()', '()', '((()))', '()', '()', '()', '((()))', '()', '()', '(())'], ['(()(()))', '()', '(()())'], ['((()())())', '()'], ['((()(((())(())((()((()))))())(()()()()()())((())(()))()())()((()))))', '(((())))', '(())'], ['((())())', '()', '(())', '(()(())())'], ['((((((())((()((()())))))))))'], ['(((())())())'], ['(())', '((()(((())))((()()))(((((())(())()(())(())((())((()(((())(())((()((())))())(()()()()()())((())(()))()())()((()))))(((())))(()))))()))((())))))'], ['()', '(())', '(()())', '()', '()', '()'], ['(())', '(()())', '()', '((((((()))))))'], ['()'], ['((()))'], ['((()((())()(()))))', '(((())))', '(())'], ['((()((()((()((())()(()))))(((())))(()))()(()))))', '(((())))', '(())'], ['()', '()', '(())', '((()))', '()', '(()()())', '((()())())', '()', '(()()()()()())'], ['(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '(((())))', '(((())()(())))', '(((())))', '(((())))', '()', '()', '()', '()', '()'], ['((()())())', '()', '(()()()()()())'], ['((()))', '(()()())'], ['((()))', '(())'], ['(((())))'], ['((()((()()))))', '()', '(()()())', '()', '(()())'], ['(((((((()(((()()))))))))))'], ['((()))', '()', '(())'], ['()', '()', '()', '()', '()', '()', '((()))', '()', '()', '()', '((()))', '()', '()', '()', '((()))', '()', '()', '()', '((()))', '()', '()', '()'], ['(()())', '()'], ['((())(()(()(()))()(()())()()((()))()()()((()))()))', '()'], ['((()((())()(()))))', '(((())))', '(())'], ['((()())())', '()', '(()(((()())()(()()))()(()())()()((())))()()()((())())()()()())'], ['(((()())))'], ['(())', '()', '()', '()', '((()))', '()', '()', '()']] for i, (inp, exp) in enumerate(zip(inputs, results)): assertion(candidate(*inp), exp, 0) check(separate_paren_groups) print('___SENTINEL___')	ced185518f5275e762c23c7a190b5c2c
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-4 if not exact_match and atol != 0: assert np.allclose(out, exp, rtol=1e-04, atol=atol) else: assert exact_match def check(candidate): inputs = [['( ) (( )) (( )( ))']] results = [['()', '(())', '(()())']] for i, (inp, exp) in enumerate(zip(inputs, results)): assertion(candidate(*inp), exp, 0) check(separate_paren_groups) print('___SENTINEL___')	230be21f8931cdba2ff9386bf38cb151
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], [1.7558893686701653], [0.9650030856354603], [0.984438743601213], [10.488539525905054], [1.4874349659668864], [0.7636841418734611], [1.0550866750250807], [2.333802704974898], [0.05907618779899971], [10.340562837282636], [14.594425286718018], [2.0662932553410105], [0.31915096006147237], [0.18237813427406302], [1.3346032282437121], [1.8553603762907702], [2.332961000883547], [0.021680163914408097], [3.5819493135932516], [0.8694186154267374], [11.915419340683165], [1.261530828471214], [4.563415501784793], [2.6876956198100403], [0.6637488925155729], [11.020840370441608], [1.2298279240135677], [3.111878426342652], [1.3280193020046864], [3.33346058589665], [4.216338505526772], [1.8986838916527826], [0.9469650857716427], [0.9128948300399008], [1.2816583458068265], [6.174933571987024], [2.609939684332363], [0.8754599167744213], [8.9080357328357], [0.2010885708651687], [6.274114559041777], [12.855609011975265], [11.056395819673522], [16.92988868999689], [1.1490031104601102], [0.7385135848830934], [2.9996633588901283], [1.4811920749857561], [0.5472841211344637], [2.136405572849686], [4.989795959151301], 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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) print('___SENTINEL___')	6cb3c1924c95504d4855298063af76d2
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-4 if not exact_match and atol != 0: assert np.allclose(out, exp, rtol=1e-04, atol=atol) else: assert exact_match def check(candidate): inputs = [[3.5]] results = [0.5] for i, (inp, exp) in enumerate(zip(inputs, results)): assertion(candidate(*inp), exp, 0) check(truncate_number) print('___SENTINEL___')	616240ae47eb93432193ae340ef52e2f
"import numpy as np\n\ndef is_floats(x) -> bool:\n # check if it is float; List[float]; Tuple[flo(...TRUNCATED)	2dc70ea59e61e184fdb215423468ef7d
"import numpy as np\n\ndef is_floats(x) -> bool:\n # check if it is float; List[float]; Tuple[flo(...TRUNCATED)	cc6c03ad1e3e2f0d1ea6a019ced8b518
"import numpy as np\n\ndef is_floats(x) -> bool:\n # check if it is float; List[float]; Tuple[flo(...TRUNCATED)	3132d9854938380be1f658c5fbe8057f
"import numpy as np\n\ndef is_floats(x) -> bool:\n # check if it is float; List[float]; Tuple[flo(...TRUNCATED)	4fdde8504554399a8630a5ea612ef0bf

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