


import random






class 							A					:
					'''simple docstring'''






					def __init__(self					:		List[str]   ,      _UpperCAmelCase					:		float   ,      _UpperCAmelCase					:		float	)		->     Optional[Any]:




							"""simple docstring"""


							lowercase__      =	x
							lowercase__      =	y

					def 						lowerCamelCase__  (self					:		Optional[Any]	)		->     Dict:




							"""simple docstring"""


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


					@classmethod
					def 						lowerCamelCase__  (cls					:		int	)		->     Union[str, Any]:




							"""simple docstring"""


							return cls(x=random.random()   ,      y=random.random()	)






def 				UpperCamelCase      (			__magic_name__  :							Union[str, Any]  )     ->						Tuple:
		"""simple docstring"""




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

		lowercase__      =	0
		for _ in range(__magic_name__  ):
				lowercase__      =	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

	A :	Union[str, Any]												=     'Please enter the desired number of Monte Carlo simulations: '
	A :	str												=     estimate_pi(int(input(prompt).strip()))
	print(F'An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}')





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A :	str												=     {
    'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A :	Optional[int]												=     [
	    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'NystromformerForMaskedLM',
	    'NystromformerForMultipleChoice',
	    'NystromformerForQuestionAnswering',
	    'NystromformerForSequenceClassification',
	    'NystromformerForTokenClassification',
	    'NystromformerLayer',
	    'NystromformerModel',
	    'NystromformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_nystromformer import (
		    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    NystromformerForMaskedLM,
		    NystromformerForMultipleChoice,
		    NystromformerForQuestionAnswering,
		    NystromformerForSequenceClassification,
		    NystromformerForTokenClassification,
		    NystromformerLayer,
		    NystromformerModel,
		    NystromformerPreTrainedModel,
		)


else:
	import sys

	A :	Optional[int]												=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

