"""simple docstring"""



import random

class SCREAMING_SNAKE_CASE     :







 """simple docstring"""


 def __init__(   self		:      str					,   _snake_case		:      float					,   _snake_case		:      float				)				->			None:




    '''simple docstring'''






    a__     				=		x
    a__     				=		y


 def        _lowerCAmelCase				(   self		:      Any				)				->			bool:




    '''simple docstring'''






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



 @classmethod
 def        _lowerCAmelCase				(   cls		:      Any				)				->			Optional[Any]:




    '''simple docstring'''






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





def   _lowerCamelCase ( UpperCAmelCase__					)							->     float:


   '''simple docstring'''


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

   a__     				=		0
   for _ in range(UpperCAmelCase__					):
      a__     				=		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

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



"""simple docstring"""



from typing import TYPE_CHECKING

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


__magic_name__    				=					{
    "configuration_donut_swin": ["DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "DonutSwinConfig"],
    "processing_donut": ["DonutProcessor"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__    				=					[
         "DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DonutSwinModel",
         "DonutSwinPreTrainedModel",
     ]

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__    				=					["DonutFeatureExtractor"]
     __magic_name__    				=					["DonutImageProcessor"]


if TYPE_CHECKING:
     from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
     from .processing_donut import DonutProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_donut_swin import (
              DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
              DonutSwinModel,
              DonutSwinPreTrainedModel,
          )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_donut import DonutFeatureExtractor
          from .image_processing_donut import DonutImageProcessor

else:
     import sys

     __magic_name__    				=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


