
'''simple docstring'''

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




def 				UpperCamelCase(					UpperCAmelCase_     ):

 # A local function to see if a dot lands in the circle.
 def is_in_circle(UpperCAmelCase_  ,	UpperCAmelCase_     ) -> bool:
  UpperCAmelCase	:     Union[str, Any]						 =			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
 UpperCAmelCase	:     List[Any]						 =			mean(
     int(is_in_circle(uniform(-1.0  ,	1.0     )  ,	uniform(-1.0  ,	1.0     )     )     )
     for _ in range(UpperCAmelCase_     )     )
 # The ratio of the area for circle to square is pi/4.
 UpperCAmelCase	:     List[str]						 =			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 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_ = 0.0  ,	UpperCAmelCase_ = 1.0  ,	):
 return mean(
     function_to_integrate(uniform(UpperCAmelCase_  ,	UpperCAmelCase_     )     ) for _ in range(UpperCAmelCase_     )     ) * (max_value - min_value)




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_ = 0.0  ,	UpperCAmelCase_ = 1.0     ):

 def identity_function(UpperCAmelCase_     ) -> float:
  return x

 UpperCAmelCase	:     Any						 =			area_under_curve_estimator(
     UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     )
 UpperCAmelCase	:     List[Any]						 =			(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 				UpperCamelCase(					UpperCAmelCase_     ):

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

 UpperCAmelCase	:     Optional[int]						 =			area_under_curve_estimator(
     UpperCAmelCase_  ,	UpperCAmelCase_  ,	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 json

from tqdm import tqdm




def 				UpperCamelCase(					):
 UpperCAmelCase	:     List[Any]						 =			argparse.ArgumentParser()

 # Required parameters
 parser.add_argument(
     '--src_path'  ,	type=UpperCAmelCase_  ,	default='biencoder-nq-dev.json'  ,	help='Path to raw DPR training data'  ,	)
 parser.add_argument(
     '--evaluation_set'  ,	type=UpperCAmelCase_  ,	help='where to store parsed evaluation_set file'  ,	)
 parser.add_argument(
     '--gold_data_path'  ,	type=UpperCAmelCase_  ,	help='where to store parsed gold_data_path file'  ,	)
 UpperCAmelCase	:     int						 =			parser.parse_args()

 with open(args.src_path  ,	'r'     ) as src_file, open(args.evaluation_set  ,	'w'     ) as eval_file, open(
     args.gold_data_path  ,	'w'     ) as gold_file:
  UpperCAmelCase	:     int						 =			json.load(UpperCAmelCase_     )
  for dpr_record in tqdm(UpperCAmelCase_     ):
   UpperCAmelCase	:     Any						 =			dpr_record['question']
   UpperCAmelCase	:     List[str]						 =			[context['title'] for context in dpr_record['positive_ctxs']]
   eval_file.write(question + '\n'     )
   gold_file.write('\t'.join(UpperCAmelCase_     ) + '\n'     )


if __name__ == "__main__":
 main()




