

import argparse
import json
import re
import string
import sys
from collections import Counter




def      UpperCAmelCase__							(     _A :					Dict				):





  '''simple docstring'''







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

  def white_space_fix(_A :					Dict				):
    return " ".join(text.split()				)

  def remove_punc(_A :					List[Any]				):
    a__    =set(string.punctuation				)
    return "".join(ch for ch in text if ch not in exclude				)

  def lower(_A :					Union[str, Any]				):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(_snake_case				)				)				)				)




def      UpperCAmelCase__							(     _A :					Union[str, Any]      , _A :					List[str]				):





  '''simple docstring'''







  a__    =normalize_answer(_snake_case				).split()
  a__    =normalize_answer(_snake_case				).split()
  a__    =Counter(_snake_case				) & Counter(_snake_case				)
  a__    =sum(common.values()				)
  if num_same == 0:
    return 0
  a__    =1.0 * num_same / len(_snake_case				)
  a__    =1.0 * num_same / len(_snake_case				)
  a__    =(2 * precision * recall) / (precision + recall)
  return fa




def      UpperCAmelCase__							(     _A :					Any      , _A :					int				):





  '''simple docstring'''







  return normalize_answer(_snake_case				) == normalize_answer(_snake_case				)




def      UpperCAmelCase__							(     _A :					Any      , _A :					str      , _A :					Any				):





  '''simple docstring'''







  a__    =[]
  for ground_truth in ground_truths:
    a__    =metric_fn(_snake_case      , _snake_case				)
    scores_for_ground_truths.append(_snake_case				)
  return max(_snake_case				)




def      UpperCAmelCase__							(     _A :					Optional[Any]      , _A :					Tuple				):





  '''simple docstring'''







  a__    =0
  a__    =[]
  for passage in dataset:
    for qa in passage["qas"]:
      total += 1
      if qa["id"] not in predictions:
        a__    =F"""Unanswered question {qa["id"]} will receive score 0."""
        print(_snake_case      , file=sys.stderr				)
        continue

      a__    =[x['''text'''] for x in qa['''answers''']]
      a__    =predictions[qa['''id''']]

      a__    =metric_max_over_ground_truths(_snake_case      , _snake_case      , _snake_case				)
      if int(_exact_match				) == 1:
        correct_ids.append(qa['''id''']				)
      exact_match += _exact_match

      fa += metric_max_over_ground_truths(_snake_case      , _snake_case      , _snake_case				)

  a__    =exact_match / total
  a__    =fa / total

  return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
       lowerCamelCase 	=   '''1.0'''
       lowerCamelCase 	=   argparse.ArgumentParser('''Official evaluation script for ReCoRD v1.0.''')
       parser.add_argument('''data_file''', help='''The dataset file in JSON format.''')
       parser.add_argument('''pred_file''', help='''The model prediction file in JSON format.''')
       parser.add_argument('''--output_correct_ids''', action='''store_true''', help='''Output the correctly answered query IDs.''')
       lowerCamelCase 	=   parser.parse_args()

       with open(args.data_file) as data_file:
              lowerCamelCase 	=   json.load(data_file)
              if dataset_json["version"] != expected_version:
                     print(
                         f"""Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json["version"]}""",
                         file=sys.stderr,
                     )
              lowerCamelCase 	=   dataset_json['''data''']

       with open(args.pred_file) as pred_file:
              lowerCamelCase 	=   json.load(pred_file)

       lowerCamelCase 	=   evaluate(dataset, predictions)

       if args.output_correct_ids:
              print(f"""Output {len(correct_ids)} correctly answered question IDs.""")
              with open('''correct_ids.json''', '''w''') as f:
                     json.dump(correct_ids, f)




"""simple docstring"""





import math as m


def   _snake_case    (    _snake_case					:      List[Any]			, _snake_case					:      Tuple			, _snake_case					:      Tuple=0.001      ):
      return (f(a + h      ) - f(a - h      )) / (2 * h)


def   _snake_case    (    _snake_case					:      int			, _snake_case					:      List[Any]=0			, _snake_case					:      Any=100			, _snake_case					:      Any=0.0001			, _snake_case					:      Optional[Any]=1E-6			, _snake_case					:      Any=False      ):
      lowerCAmelCase       :						Optional[Any]							= xa  # set the initial guess
      lowerCAmelCase       :						str							= [a]
      lowerCAmelCase       :						List[str]							= abs(f(_snake_case      )      )
      lowerCAmelCase       :						Dict							= lambda _snake_case      : calc_derivative(_snake_case			, _snake_case			, h=_snake_case      )  # noqa: E731  Derivative of f(x)
      for _ in range(_snake_case      ):
            if fa(_snake_case      ) == 0:
                  raise ValueError('''No converging solution found'''      )
            lowerCAmelCase       :						Tuple							= a - f(_snake_case      ) / fa(_snake_case      )  # Calculate the next estimate
            if logsteps:
                  steps.append(_snake_case      )
            if error < maxerror:
                  break
      else:
            raise ValueError('''Iteration limit reached, no converging solution found'''      )
      if logsteps:
            # If logstep is true, then log intermediate steps
            return a, error, steps
      return a, error


if __name__ == "__main__":
 from matplotlib import pyplot as plt

 snake_case__		:  Any     					=					lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
 snake_case__					,  snake_case__					,  snake_case__		:  List[str]     					=					newton_raphson(
     f, xa=10, maxiter=1_000, step=1e-6, logsteps=True
 )
 plt.plot([abs(f(x)) for x in steps])
 plt.xlabel('''step''')
 plt.ylabel('''error''')
 plt.show()
 print(f"""solution = {{{solution:f}}}, error = {{{error:f}}}""")


