



'''simple docstring'''





import argparse
import json
import re
import string
import sys

import numpy as np


_UpperCamelCase							    =						0.5

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	)				->				int:
  __lowerCamelCase	:	Tuple   	=			['.', ',', ';', ':']

  for token in remove_tokens:
    __lowerCamelCase	:	List[str]   	=			ground_truth.replace(_lowerCAmelCase	,''	)
    __lowerCamelCase	:	Union[str, Any]   	=			prediction.replace(_lowerCAmelCase	,''	)

  __lowerCamelCase	:	int   	=			ground_truth.lower(), prediction.lower()
  __lowerCamelCase	:	List[Any]   	=			ground_truth.replace('/'	,' '	), prediction.replace('/'	,' '	)
  __lowerCamelCase	:	Optional[Any]   	=			set(ground_truth.split(' '	)	), set(prediction.split(' '	)	)

  __lowerCamelCase	:	Tuple   	=			ground_truth.intersection(_lowerCAmelCase	)
  __lowerCamelCase	:	Optional[Any]   	=			ground_truth.union(_lowerCAmelCase	)
  __lowerCamelCase	:	Optional[int]   	=			len(_lowerCAmelCase	) / len(_lowerCAmelCase	)
  return jaccard

def 	a_					(							_lowerCAmelCase	)				->				int:

  def remove_articles(_lowerCAmelCase	):
    return re.sub(r'\b(a|an|the)\b'	,' '	,_lowerCAmelCase	)

  def white_space_fix(_lowerCAmelCase	):
    return " ".join(text.split()	)

  def remove_punc(_lowerCAmelCase	):
    __lowerCamelCase	:	Union[str, Any]   	=			set(string.punctuation	)
    return "".join(ch for ch in text if ch not in exclude	)

  def lower(_lowerCAmelCase	):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(_lowerCAmelCase	)	)	)	)

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)				->				Union[str, Any]:
  __lowerCamelCase	:	Optional[Any]   	=			0, 0, 0

  __lowerCamelCase	:	Dict   	=			'Parties' in qa_id

  # first check if ground truth is empty
  if len(_lowerCAmelCase	) == 0:
    if len(_lowerCAmelCase	) > 0:
      fp += len(_lowerCAmelCase	)  # false positive for each one
  else:
    for ground_truth in ground_truths:
      assert len(_lowerCAmelCase	) > 0
      # check if there is a match
      __lowerCamelCase	:	Tuple   	=			False
      for pred in predictions:
        if substr_ok:
          __lowerCamelCase	:	str   	=			get_jaccard(_lowerCAmelCase	,_lowerCAmelCase	) >= IOU_THRESH or ground_truth in pred
        else:
          __lowerCamelCase	:	str   	=			get_jaccard(_lowerCAmelCase	,_lowerCAmelCase	) >= IOU_THRESH
        if is_match:
          __lowerCamelCase	:	List[Any]   	=			True

      if match_found:
        tp += 1
      else:
        fn += 1

        # now also get any fps by looping through preds
    for pred in predictions:
      # Check if there's a match. if so, don't count (don't want to double count based on the above)
      # but if there's no match, then this is a false positive.
      # (Note: we get the true positives in the above loop instead of this loop so that we don't double count
      # multiple predictions that are matched with the same answer.)
      __lowerCamelCase	:	Tuple   	=			False
      for ground_truth in ground_truths:
        assert len(_lowerCAmelCase	) > 0
        if substr_ok:
          __lowerCamelCase	:	List[str]   	=			get_jaccard(_lowerCAmelCase	,_lowerCAmelCase	) >= IOU_THRESH or ground_truth in pred
        else:
          __lowerCamelCase	:	Any   	=			get_jaccard(_lowerCAmelCase	,_lowerCAmelCase	) >= IOU_THRESH
        if is_match:
          __lowerCamelCase	:	str   	=			True

      if not match_found:
        fp += 1

  __lowerCamelCase	:	List[Any]   	=			tp / (tp + fp) if tp + fp > 0 else np.nan
  __lowerCamelCase	:	Tuple   	=			tp / (tp + fn) if tp + fn > 0 else np.nan

  return precision, recall

def 	a_					(							_lowerCAmelCase	)				->				List[Any]:
  __lowerCamelCase	:	Union[str, Any]   	=			precisions[::-1]
  for i in range(1	,len(_lowerCAmelCase	)	):
    __lowerCamelCase	:	Dict   	=			max(precision_best[i - 1]	,precision_best[i]	)
  __lowerCamelCase	:	int   	=			precision_best[::-1]
  return precisions

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	)				->				Optional[Any]:
  __lowerCamelCase	:	Tuple   	=			process_precisions(_lowerCAmelCase	)
  __lowerCamelCase	:	Optional[int]   	=			np.trapz(_lowerCAmelCase	,_lowerCAmelCase	)
  if np.isnan(_lowerCAmelCase	):
    return 0
  return aupr

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)				->				Optional[Any]:
  __lowerCamelCase	:	Any   	=			process_precisions(_lowerCAmelCase	)
  __lowerCamelCase	:	List[str]   	=			0
  for prec, recall in zip(_lowerCAmelCase	,_lowerCAmelCase	):
    if recall >= recall_thresh:
      __lowerCamelCase	:	str   	=			prec
      break
  return prec_at_recall

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	)				->				Union[str, Any]:
  return normalize_answer(_lowerCAmelCase	) == normalize_answer(_lowerCAmelCase	)

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)				->				Optional[Any]:
  __lowerCamelCase	:	Optional[int]   	=			0
  for pred in predictions:
    for ground_truth in ground_truths:
      __lowerCamelCase	:	str   	=			metric_fn(_lowerCAmelCase	,_lowerCAmelCase	)
      if score == 1:  # break the loop when one prediction matches the ground truth
        break
    if score == 1:
      break
  return score

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	)				->				List[Any]:
  __lowerCamelCase	:	Optional[int]   	=			0
  __lowerCamelCase	:	Tuple   	=			[]
  __lowerCamelCase	:	int   	=			[]
  for article in dataset:
    for paragraph in article["paragraphs"]:
      for qa in paragraph["qas"]:
        total += 1
        if qa["id"] not in predictions:
          __lowerCamelCase	:	int   	=			'Unanswered question ' + qa['id'] + ' will receive score 0.'
          print(_lowerCAmelCase	,file=sys.stderr	)
          continue
        __lowerCamelCase	:	Union[str, Any]   	=			[x['text'] for x in qa['answers']]
        __lowerCamelCase	:	Optional[Any]   	=			predictions[qa['id']]
        __lowerCamelCase	:	Tuple   	=			compute_precision_recall(_lowerCAmelCase	,_lowerCAmelCase	,qa['id']	)

        precisions.append(_lowerCAmelCase	)
        recalls.append(_lowerCAmelCase	)

        if precision == 0 and recall == 0:
          fa += 0
        else:
          fa += 2 * (precision * recall) / (precision + recall)

        exact_match += metric_max_over_ground_truths(_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)

  __lowerCamelCase	:	List[str]   	=			[x for _, x in sorted(zip(_lowerCAmelCase	,_lowerCAmelCase	)	)]
  recalls.sort()

  __lowerCamelCase	:	int   	=			100.0 * fa / total
  __lowerCamelCase	:	List[str]   	=			100.0 * exact_match / total
  __lowerCamelCase	:	Optional[int]   	=			get_aupr(_lowerCAmelCase	,_lowerCAmelCase	)

  __lowerCamelCase	:	Any   	=			get_prec_at_recall(_lowerCAmelCase	,_lowerCAmelCase	,recall_thresh=0.9	)
  __lowerCamelCase	:	int   	=			get_prec_at_recall(_lowerCAmelCase	,_lowerCAmelCase	,recall_thresh=0.8	)

  return {
      "exact_match": exact_match,
      "f1": fa,
      "aupr": aupr,
      "prec_at_80_recall": prec_at_aa_recall,
      "prec_at_90_recall": prec_at_aa_recall,
  }


if __name__ == "__main__":
   _UpperCamelCase							    =						argparse.ArgumentParser(description='Evaluation for CUAD')
   parser.add_argument('dataset_file', help='Dataset file')
   parser.add_argument('prediction_file', help='Prediction File')
   _UpperCamelCase							    =						parser.parse_args()
   with open(args.dataset_file) as dataset_file:
      _UpperCamelCase							    =						json.load(dataset_file)
      _UpperCamelCase							    =						dataset_json['data']
   with open(args.prediction_file) as prediction_file:
      _UpperCamelCase							    =						json.load(prediction_file)
   print(json.dumps(evaluate(dataset, predictions)))










'''simple docstring'''





import random






class  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     Union[str, Any]      ,     _a				:     float      ,     _a				:     float       ) -> None:
    __lowerCamelCase	:	Optional[int]   	=			x
    __lowerCamelCase	:	Union[str, Any]   	=			y




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




  @classmethod
  def 						_lowercase					(			cls				:     List[Any]       ) -> Optional[int]:
    return cls(x=random.random()      ,     y=random.random()       )




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

  __lowerCamelCase	:	Optional[int]   	=			0
  for _ in range(_lowerCAmelCase	):
    __lowerCamelCase	:	Dict   	=			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)}''')






