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
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)}''')