

import random



class SCREAMING_SNAKE_CASE	:
  """simple docstring"""






  def __init__(			self :    int       ,							lowerCAmelCase :    float       ,							lowerCAmelCase :    float    )							->	None:






     """simple docstring"""







     __lowerCAmelCase  :			List[Any]					    =						x
     __lowerCAmelCase  :			str					    =						y







  def        SCREAMING_SNAKE_CASE    (			self :    str    )							->	bool:






     """simple docstring"""







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







  @classmethod
  def        SCREAMING_SNAKE_CASE    (			cls :    Dict    )							->	Tuple:






     """simple docstring"""







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






def 	snake_case_      (__A     :						int     )			->	float:
   if number_of_simulations < 1:
      raise ValueError("""At least one simulation is necessary to estimate PI."""     )

   __lowerCAmelCase  :			Optional[Any]					    =						0
   for _ in range(__A     ):
      __lowerCAmelCase  :			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

   __UpperCAmelCase      = """Please enter the desired number of Monte Carlo simulations: """
   __UpperCAmelCase      = estimate_pi(int(input(prompt).strip()))
   print(F'An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}')








import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()



def 							_A   (						lowerCAmelCase_     :					Optional[Any]   ,				lowerCAmelCase_     :					Tuple			):
     """simple docstring"""

     lowerCAmelCase__		= CanineConfig()
     lowerCAmelCase__		= CanineModel(lowerCAmelCase_			)
     model.eval()

     print(F'Building PyTorch model from configuration: {config}'			)

     # Load weights from tf checkpoint
     load_tf_weights_in_canine(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_			)

     # Save pytorch-model (weights and configuration)
     print(F'Save PyTorch model to {pytorch_dump_path}'			)
     model.save_pretrained(lowerCAmelCase_			)

     # Save tokenizer files
     lowerCAmelCase__		= CanineTokenizer()
     print(F'Save tokenizer files to {pytorch_dump_path}'			)
     tokenizer.save_pretrained(lowerCAmelCase_			)


if __name__ == "__main__":
     UpperCamelCase								=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--tf_checkpoint_path',
         default=None,
         type=str,
         required=True,
         help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
     )
     parser.add_argument(
         '--pytorch_dump_path',
         default=None,
         type=str,
         required=True,
         help='Path to a folder where the PyTorch model will be placed.',
     )
     UpperCamelCase								=     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
