import random







class 			SCREAMING_SNAKE_CASE__ :

       def __init__(				self :			Optional[Any]       ,       _lowerCAmelCase :			Optional[Any]       ,       _lowerCAmelCase :			Union[str, Any]       ):
           __snake_case    :							List[str]             =  x
           __snake_case    :							Dict             =  y

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





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







def  __lowerCAmelCase							(						__SCREAMING_SNAKE_CASE    : int					):







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

    __snake_case    :							Any             =  0
    for _ in range(__SCREAMING_SNAKE_CASE					):
        __snake_case    :							Tuple             =  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

     lowercase_   				=       "Please enter the desired number of Monte Carlo simulations: "
     lowercase_   				=       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_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_						=					{'configuration_hubert': ['HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'HubertConfig']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_						=					[
					    'HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'HubertForCTC',
					    'HubertForSequenceClassification',
					    'HubertModel',
					    'HubertPreTrainedModel',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_						=					[
					    'TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFHubertForCTC',
					    'TFHubertModel',
					    'TFHubertPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_hubert import (
										    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    HubertForCTC,
										    HubertForSequenceClassification,
										    HubertModel,
										    HubertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_hubert import (
										    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFHubertForCTC,
										    TFHubertModel,
										    TFHubertPreTrainedModel,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE_						=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


