
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean







def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       Optional[Any]:





  """simple docstring"""

  def is_in_circle(__SCREAMING_SNAKE_CASE						:  float			,     __SCREAMING_SNAKE_CASE						:  float ) -> bool:
    __A		:     int												=		sqrt((x**2) + (y**2) )
    # Our circle has a radius of 1, so a distance
    # greater than 1 would land outside the circle.
    return distance_from_centre <= 1

  # The proportion of guesses that landed in the circle
  __A		:     str												=		mean(
      int(is_in_circle(uniform(-1.0			,     1.0 )			,     uniform(-1.0			,     1.0 ) ) )
      for _ in range(__SCREAMING_SNAKE_CASE ) )
  # The ratio of the area for circle to square is pi/4.
  __A		:     Tuple												=		proportion * 4
  print(F"The estimated value of pi is {pi_estimate}" )
  print(F"The numpy value of pi is {pi}" )
  print(F"The total error is {abs(pi - pi_estimate )}" )







def        A_     (						__SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  Callable[[float], float]			,     __SCREAMING_SNAKE_CASE						:  float = 0.0			,     __SCREAMING_SNAKE_CASE						:  float = 1.0			,     )				->       float:





  """simple docstring"""

  return mean(
      function_to_integrate(uniform(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ) ) for _ in range(__SCREAMING_SNAKE_CASE ) ) * (max_value - min_value)







def        A_     (						__SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  float = 0.0			,     __SCREAMING_SNAKE_CASE						:  float = 1.0 )				->       None:





  """simple docstring"""

  def identity_function(__SCREAMING_SNAKE_CASE						:  float ) -> float:
    return x

  __A		:     int												=		area_under_curve_estimator(
      __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
  __A		:     Optional[int]												=		(max_value * max_value - min_value * min_value) / 2

  print("""******************""" )
  print(F"Estimating area under y=x where x varies from {min_value} to {max_value}" )
  print(F"Estimated value is {estimated_value}" )
  print(F"Expected value is {expected_value}" )
  print(F"Total error is {abs(estimated_value - expected_value )}" )
  print("""******************""" )







def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       None:





  """simple docstring"""

  def function_to_integrate(__SCREAMING_SNAKE_CASE						:  float ) -> float:
    return sqrt(4.0 - x * x )

  __A		:     str												=		area_under_curve_estimator(
      __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     0.0			,     2.0 )

  print("""******************""" )
  print("""Estimating pi using area_under_curve_estimator""" )
  print(F"Estimated value is {estimated_value}" )
  print(F"Expected value is {pi}" )
  print(F"Total error is {abs(estimated_value - pi )}" )
  print("""******************""" )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





'''simple docstring'''
import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()







def        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[Any]			,     __SCREAMING_SNAKE_CASE						:  List[str]			,     __SCREAMING_SNAKE_CASE						:  Optional[Any] )				->       List[Any]:





  """simple docstring"""

  __A		:     Optional[int]												=		RemBertConfig.from_json_file(__SCREAMING_SNAKE_CASE )
  print("""Building PyTorch model from configuration: {}""".format(str(__SCREAMING_SNAKE_CASE ) ) )
  __A		:     str												=		RemBertModel(__SCREAMING_SNAKE_CASE )

  # Load weights from tf checkpoint
  load_tf_weights_in_rembert(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

  # Save pytorch-model
  print("""Save PyTorch model to {}""".format(__SCREAMING_SNAKE_CASE ) )
  torch.save(model.state_dict()			,     __SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
     A__   :		Optional[Any] 		=argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
     )
     parser.add_argument(
         '--rembert_config_file',
         default=None,
         type=str,
         required=True,
         help=(
             'The config json file corresponding to the pre-trained RemBERT model. \n'
             'This specifies the model architecture.'
         ),
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     A__   :		Dict 		=parser.parse_args()
     convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)



