code: import copy
  
  from transformers.configuration_utils import PretrainedConfig
  from transformers.utils import logging
  
  
  a     :			Optional[int]					    =						logging.get_logger(__name__)
  
  class 				_lowercase						(    UpperCAmelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							List[str]        =						'hybrid-clip'
      SCREAMING_SNAKE_CASE:							str        =						True
  
  
  
  
      def __init__(						self					,	lowerCamelCase__=512					,	**lowerCamelCase__   ):
         super().__init__(**lowerCamelCase__   )
  
         if "text_config" not in kwargs:
            raise ValueError("`text_config` can not be `None`."   )
  
         if "vision_config" not in kwargs:
            raise ValueError("`vision_config` can not be `None`."   )
  
         lowerCAmelCase_:       Tuple         =  kwargs.pop("text_config"   )
         lowerCAmelCase_:       Dict         =  kwargs.pop("vision_config"   )
  
         lowerCAmelCase_:       Any         =  text_config.pop("model_type"   )
         lowerCAmelCase_:       Any         =  vision_config.pop("model_type"   )
  
         from transformers import AutoConfig
  
         lowerCAmelCase_:       Optional[int]         =  AutoConfig.for_model(lowerCamelCase__					,	**lowerCamelCase__   )
  
         if vision_model_type == "clip":
            lowerCAmelCase_:       Dict         =  AutoConfig.for_model(lowerCamelCase__					,	**lowerCamelCase__   ).vision_config
         elif vision_model_type == "clip_vision_model":
            from transformers import CLIPVisionConfig
  
            lowerCAmelCase_:       Dict         =  CLIPVisionConfig(**lowerCamelCase__   )
         else:
            lowerCAmelCase_:       Any         =  AutoConfig.for_model(lowerCamelCase__					,	**lowerCamelCase__   )
  
         lowerCAmelCase_:       int         =  projection_dim
         lowerCAmelCase_:       Any         =  1.0
  
  
  
  
      @classmethod
      def     _a						(						cls					,	lowerCamelCase__					,	lowerCamelCase__					,	**lowerCamelCase__   ):
         return cls(text_config=text_config.to_dict()					,	vision_config=vision_config.to_dict()					,	**lowerCamelCase__   )
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       Optional[int]         =  copy.deepcopy(self.__dict__   )
         lowerCAmelCase_:       Tuple         =  self.text_config.to_dict()
         lowerCAmelCase_:       Union[str, Any]         =  self.vision_config.to_dict()
         lowerCAmelCase_:       Union[str, Any]         =  self.__class__.model_type
         return output
style_context: import random
  
  class 				_lowercase						:
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__   ):
         lowerCAmelCase_:       int         =  x
         lowerCAmelCase_:       List[str]         =  y
  
  
  
  
      def     _a						(						self   ):
         return (self.x**2 + self.y**2) <= 1
  
  
  
  
      @classmethod
      def     _a						(						cls   ):
         return cls(x=random.random()					,	y=random.random()   )
  
  
  
  
  
  def 		snake_case__  (		lowercase				):
     if number_of_simulations < 1:
        raise ValueError("At least one simulation is necessary to estimate PI."				)
  
     lowerCAmelCase_:       List[Any]         =  0
     for _ in range(lowercase				):
        lowerCAmelCase_:       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
  
   a     :			Union[str, Any]					    =						"""Please enter the desired number of Monte Carlo simulations: """
   a     :			int					    =						estimate_pi(int(input(prompt).strip()))
   print(f'''An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}''')