

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class      lowerCamelCase_ (			_lowerCAmelCase):
 _lowerCAmelCase									=	["""image_processor""", """tokenizer"""]
 _lowerCAmelCase									=	"""EfficientNetImageProcessor"""
 _lowerCAmelCase									=	("""BertTokenizer""", """BertTokenizerFast""")




 def __init__(			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   super().__init__(lowerCamelCase__  ,  lowerCamelCase__							)




 def __call__(			self  ,  _UpperCAmelCase=None  ,  _UpperCAmelCase=None  ,  _UpperCAmelCase="max_length"  ,  _UpperCAmelCase=64  ,  _UpperCAmelCase=None  ,  **_UpperCAmelCase							):

   if text is None and images is None:
     raise ValueError('You have to specify either text or images. Both cannot be none.'							)

   if text is not None:
     __UpperCAmelCase	:  str		=     self.tokenizer(
         lowerCamelCase__  ,  padding=lowerCamelCase__  ,  max_length=lowerCamelCase__  ,  return_tensors=lowerCamelCase__  ,  **lowerCamelCase__							)

   if images is not None:
     __UpperCAmelCase	:  int		=     self.image_processor(lowerCamelCase__  ,  return_tensors=lowerCamelCase__  ,  **lowerCamelCase__							)

   if text is not None and images is not None:
     __UpperCAmelCase	:  int		=     image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   else:
     return BatchEncoding(data=dict(**lowerCamelCase__							)  ,  tensor_type=lowerCamelCase__							)




 def     a       (			self  ,  *_UpperCAmelCase  ,  **_UpperCAmelCase							):
   return self.tokenizer.batch_decode(*lowerCamelCase__  ,  **lowerCamelCase__							)




 def     a       (			self  ,  *_UpperCAmelCase  ,  **_UpperCAmelCase							):
   return self.tokenizer.decode(*lowerCamelCase__  ,  **lowerCamelCase__							)




 @property
 def     a       (			self							):
   __UpperCAmelCase	:  Optional[Any]		=     self.tokenizer.model_input_names
   __UpperCAmelCase	:  Optional[int]		=     self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)













import random




class 							__A	:





				"""simple docstring"""







				def __init__(					self				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Optional[Any]									=x
									__UpperCamelCase :   int									=y


				def  __lowercase				(					self       ):




									"""simple docstring"""

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







				@classmethod
				def  __lowercase				(					cls       ):




									"""simple docstring"""

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






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

					__UpperCamelCase :   List[str]									=0
					for _ in range(a_   ):
										__UpperCamelCase :   Optional[int]									=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_			:Optional[int]	    =		'''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)}")

