import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
from ..clip.configuration_clip import CLIPVisionConfig


lowercase__      :    str          =     logging.get_logger(__name__)




class        lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	UpperCAmelCase_  : Optional[int]       =    """vision-text-dual-encoder"""
	UpperCAmelCase_  : List[str]       =    True


	def __init__(							self		,      __SCREAMING_SNAKE_CASE=512		,      __SCREAMING_SNAKE_CASE=2.6_5_9_2		,      **__SCREAMING_SNAKE_CASE    )						->Optional[int]:
								super().__init__(**__SCREAMING_SNAKE_CASE    )

								if "vision_config" not in kwargs:
															raise ValueError('''`vision_config` can not be `None`.'''    )

								if "text_config" not in kwargs:
															raise ValueError('''`text_config` can not be `None`.'''    )

								lowerCAmelCase     		=		kwargs.pop('''vision_config'''    )
								lowerCAmelCase     		=		kwargs.pop('''text_config'''    )

								lowerCAmelCase     		=		vision_config.pop('''model_type'''    )
								lowerCAmelCase     		=		text_config.pop('''model_type'''    )

								if vision_model_type == "clip":
															lowerCAmelCase     		=		AutoConfig.for_model(__SCREAMING_SNAKE_CASE		,      **__SCREAMING_SNAKE_CASE    ).vision_config
								elif vision_model_type == "clip_vision_model":
															lowerCAmelCase     		=		CLIPVisionConfig(**__SCREAMING_SNAKE_CASE    )
								else:
															lowerCAmelCase     		=		AutoConfig.for_model(__SCREAMING_SNAKE_CASE		,      **__SCREAMING_SNAKE_CASE    )

								lowerCAmelCase     		=		AutoConfig.for_model(__SCREAMING_SNAKE_CASE		,      **__SCREAMING_SNAKE_CASE    )

								lowerCAmelCase     		=		projection_dim
								lowerCAmelCase     		=		logit_scale_init_value


	@classmethod
	def       SCREAMING_SNAKE_CASE_    (							cls		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      **__SCREAMING_SNAKE_CASE    )						->Any:
								return cls(vision_config=vision_config.to_dict()		,      text_config=text_config.to_dict()		,      **__SCREAMING_SNAKE_CASE    )




	def       SCREAMING_SNAKE_CASE_    (							self    )						->List[str]:
								lowerCAmelCase     		=		copy.deepcopy(self.__dict__    )
								lowerCAmelCase     		=		self.vision_config.to_dict()
								lowerCAmelCase     		=		self.text_config.to_dict()
								lowerCAmelCase     		=		self.__class__.model_type
								return output




import random




class        lowercase_  :



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->None:
								lowerCAmelCase     		=		x
								lowerCAmelCase     		=		y


	def       SCREAMING_SNAKE_CASE_    (							self    )						->bool:
								return (self.x**2 + self.y**2) <= 1




	@classmethod
	def       SCREAMING_SNAKE_CASE_    (							cls    )						->Tuple:
								return cls(x=random.random()		,      y=random.random()    )

def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     float:
							if number_of_simulations < 1:
														raise ValueError('''At least one simulation is necessary to estimate PI.'''							)

							lowerCAmelCase     		=		0
							for _ in range(snake_case__							):
														lowerCAmelCase     		=		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__      :    int          =     '''Please enter the desired number of Monte Carlo simulations: '''
	lowercase__      :    Dict          =     estimate_pi(int(input(prompt).strip()))
	print(f'An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}')



