


"""simple docstring"""





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


def   _snake_case    (    _snake_case					:      Dict      ):

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

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

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

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

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


def   _snake_case    (    _snake_case					:      Union[str, Any]			, _snake_case					:      List[str]      ):
      lowerCAmelCase       :						Tuple							= normalize_answer(_snake_case      ).split()
      lowerCAmelCase       :						List[Any]							= normalize_answer(_snake_case      ).split()
      lowerCAmelCase       :						List[str]							= Counter(_snake_case      ) & Counter(_snake_case      )
      lowerCAmelCase       :						List[str]							= sum(common.values()      )
      if num_same == 0:
            return 0
      lowerCAmelCase       :						Optional[int]							= 1.0 * num_same / len(_snake_case      )
      lowerCAmelCase       :						Dict							= 1.0 * num_same / len(_snake_case      )
      lowerCAmelCase       :						Union[str, Any]							= (2 * precision * recall) / (precision + recall)
      return fa


def   _snake_case    (    _snake_case					:      Any			, _snake_case					:      int      ):
      return normalize_answer(_snake_case      ) == normalize_answer(_snake_case      )


def   _snake_case    (    _snake_case					:      Any			, _snake_case					:      str			, _snake_case					:      Any      ):
      lowerCAmelCase       :						Optional[int]							= []
      for ground_truth in ground_truths:
            lowerCAmelCase       :						Any							= metric_fn(_snake_case			, _snake_case      )
            scores_for_ground_truths.append(_snake_case      )
      return max(_snake_case      )


def   _snake_case    (    _snake_case					:      Optional[Any]			, _snake_case					:      Tuple      ):
      lowerCAmelCase       :						Union[str, Any]							= 0
      lowerCAmelCase       :						List[Any]							= []
      for passage in dataset:
            for qa in passage["qas"]:
                  total += 1
                  if qa["id"] not in predictions:
                        lowerCAmelCase       :						List[str]							= f'''Unanswered question {qa["id"]} will receive score 0.'''
                        print(_snake_case			, file=sys.stderr      )
                        continue

                  lowerCAmelCase       :						Dict							= [x['''text'''] for x in qa['''answers''']]
                  lowerCAmelCase       :						Union[str, Any]							= predictions[qa['''id''']]

                  lowerCAmelCase       :						str							= 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      )

      lowerCAmelCase       :						int							= exact_match / total
      lowerCAmelCase       :						Any							= fa / total

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


if __name__ == "__main__":
 snake_case__		:  Union[str, Any]     					=					'''1.0'''
 snake_case__		:  List[Any]     					=					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.''')
 snake_case__		:  Union[str, Any]     					=					parser.parse_args()

 with open(args.data_file) as data_file:
  snake_case__		:  List[str]     					=					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,
   )
  snake_case__		:  List[str]     					=					dataset_json['''data''']

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

 snake_case__					,  snake_case__		:  Tuple     					=					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}}}""")


