

'''simple docstring'''
import random
class  _snake_case						:






			def __init__(   self       ,_snake_case       ,_snake_case			):
				UpperCAmelCase_			:	int      =   x
				UpperCAmelCase_			:	Optional[int]      =   y






			def 						UpperCamelCase__				(   self			):
				return (self.x**2 + self.y**2) <= 1






			@classmethod
			def 						UpperCamelCase__				(   cls			):
				return cls(x=random.random()       ,y=random.random()			)




def       a__    (  _SCREAMING_SNAKE_CASE					:    int							)			-> float:


	"""simple docstring"""





	if number_of_simulations < 1:
		raise ValueError("At least one simulation is necessary to estimate PI."							)

	UpperCAmelCase_			:	Any      =   0
	for _ in range(_SCREAMING_SNAKE_CASE							):
		UpperCAmelCase_			:	Dict      =   Point.random_unit_square()

		if random_point.is_in_unit_circle():
			number_in_unit_circle += 1

	return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
					# import doctest

					# doctest.testmod()
					from math import pi

					_lowerCamelCase	 =			"""Please enter the desired number of Monte Carlo simulations: """
					_lowerCamelCase	 =			estimate_pi(int(input(prompt).strip()))
					print(f"""An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}""")










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						)



