


from math import sqrt






def 		a__       (							A__							):
			SCREAMING_SNAKE_CASE_						:			List[Any]       						= [1]
			for i in range(2,	int(sqrt(A__							)							) + 1,	1							):
						if number % i == 0:
									values.append(A__							)
									if int(number // i							) != i:
												values.append(int(number // i							)							)
			return sorted(A__							)






def 		a__       (							A__							):
			return sum(factors(A__							)							) > n






def 		a__       (							A__							):
			SCREAMING_SNAKE_CASE_						:			Optional[int]       						= factors(A__							)
			SCREAMING_SNAKE_CASE_						:			Optional[int]       						= len(A__							)
			SCREAMING_SNAKE_CASE_						:			Dict       						= [[0 for i in range(number + 1							)] for j in range(r + 1							)]
			for i in range(r + 1							):
						SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= True

			for i in range(1,	number + 1							):
						SCREAMING_SNAKE_CASE_						:			Tuple       						= False

			for i in range(1,	r + 1							):
						for j in range(1,	number + 1							):
									if j < values[i - 1]:
												SCREAMING_SNAKE_CASE_						:			Dict       						= subset[i - 1][j]
									else:
												SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

			return subset[r][number] != 0






def 		a__       (							A__							):
			return abundant(A__							) and not semi_perfect(A__							)


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)
				for number in (69, 70, 71):
								print(F"""{number} is {"" if weird(number) else "not "}weird.""")











import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
				import tensorflow as tf

				from transformers.activations_tf import get_tf_activation





@require_tf
class 		A__				(   unittest.TestCase ):






	def  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






						__magic_name__					:							int		   =							tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 1_0_0.0]						)
						__magic_name__					:							List[str]		   =							get_tf_activation('''gelu'''						)
						__magic_name__					:							str		   =							get_tf_activation('''gelu_10'''						)

						__magic_name__					:							List[str]		   =							gelu(lowerCamelCase						)
						__magic_name__					:							Optional[Any]		   =							geluaa(lowerCamelCase						)

						__magic_name__					:							Optional[int]		   =							tf.where(y_gelu_aa < 1_0.0				,     1.0				,     0.0						)

						self.assertEqual(tf.math.reduce_max(lowerCamelCase						).numpy().item()				,     1_0.0						)
						self.assertTrue(np.allclose(y_gelu * clipped_mask				,     y_gelu_aa * clipped_mask						)						)






	def  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






						get_tf_activation('''gelu'''						)
						get_tf_activation('''gelu_10'''						)
						get_tf_activation('''gelu_fast'''						)
						get_tf_activation('''gelu_new'''						)
						get_tf_activation('''glu'''						)
						get_tf_activation('''mish'''						)
						get_tf_activation('''quick_gelu'''						)
						get_tf_activation('''relu'''						)
						get_tf_activation('''sigmoid'''						)
						get_tf_activation('''silu'''						)
						get_tf_activation('''swish'''						)
						get_tf_activation('''tanh'''						)
						with self.assertRaises(lowerCamelCase						):
											get_tf_activation('''bogus'''						)
						with self.assertRaises(lowerCamelCase						):
											get_tf_activation(lowerCamelCase						)



