code: 
  
  import random
  
  
  
  
  
  
  class    __A				:
  
  
  
  
  
  
      def __init__(	self			:    Optional[Any]    ,				UpperCAmelCase_			:    float    ,				UpperCAmelCase_			:    float    ):
          lowerCAmelCase				:			List[str]    			=      x
          lowerCAmelCase				:			Union[str, Any]    			=      y
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[Any]    ):
          return (self.x**2 + self.y**2) <= 1
  
  
  
  
  
  
      @classmethod
      def 	lowercase__ (	cls			:    int    ):
          return cls(x=random.random()    ,				y=random.random()    )
  
  
  
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> float:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      if number_of_simulations < 1:
          raise ValueError('At least one simulation is necessary to estimate PI.'				)
  
      lowerCAmelCase				:			Optional[Any]    			=      0
      for _ in range(_UpperCAmelCase				):
          lowerCAmelCase				:			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
  
   __A   :     List[str]      =			'''Please enter the desired number of Monte Carlo simulations: '''
   __A   :     List[Any]      =			estimate_pi(int(input(prompt).strip()))
   print(F'An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}')
  
  
  
  
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  __A   :     Optional[int]      =			{
      '''configuration_splinter''': ['''SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SplinterConfig'''],
      '''tokenization_splinter''': ['''SplinterTokenizer'''],
  }
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     Optional[int]      =			['''SplinterTokenizerFast''']
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     Union[str, Any]      =			[
       '''SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SplinterForQuestionAnswering''',
       '''SplinterForPreTraining''',
       '''SplinterLayer''',
       '''SplinterModel''',
       '''SplinterPreTrainedModel''',
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
   from .tokenization_splinter import SplinterTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_splinter_fast import SplinterTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_splinter import (
        SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SplinterForPreTraining,
        SplinterForQuestionAnswering,
        SplinterLayer,
        SplinterModel,
        SplinterPreTrainedModel,
    )
  
  
  else:
   import sys
  
   __A   :     Optional[Any]      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  