



"""simple docstring"""






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


def 							A__ (     UpperCamelCase__    ):


  '''simple docstring'''


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

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

  def remove_punc(UpperCamelCase__    ):
    _SCREAMING_SNAKE_CASE        =			set(string.punctuation    )
    return "".join(ch for ch in text if ch not in exclude    )

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

  return white_space_fix(remove_articles(remove_punc(lower(snake_case__    )    )    )    )


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


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


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  return normalize_answer(snake_case__    ) == normalize_answer(snake_case__    )


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


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


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


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

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

      _SCREAMING_SNAKE_CASE        =			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__    )

  _SCREAMING_SNAKE_CASE        =			exact_match / total
  _SCREAMING_SNAKE_CASE        =			fa / total

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


if __name__ == "__main__":
    lowerCamelCase				:     Any								=  '1.0'
    lowerCamelCase				:     Tuple								=  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				:     Tuple								=  parser.parse_args()

    with open(args.data_file) as data_file:
        lowerCamelCase				:     Optional[int]								=  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				:     Dict								=  dataset_json['data']

    with open(args.pred_file) as pred_file:
        lowerCamelCase				:     Optional[int]								=  json.load(pred_file)

    lowerCamelCase				:     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'''







from __future__ import annotations


def     _UpperCamelCase				(  snake_case__				):
      __A		 =   [0] * len(snake_case__				)
      for i in range(1				,      len(snake_case__				)				):
            __A		 =   duration_times[i - 1] + waiting_times[i - 1]
      return waiting_times


def     _UpperCamelCase				(  snake_case__				,      snake_case__				):
      return [
          duration_time + waiting_times[i]
          for i, duration_time in enumerate(snake_case__				)
      ]


def     _UpperCamelCase				(  snake_case__				):
      return sum(snake_case__				) / len(snake_case__				)


def     _UpperCamelCase				(  snake_case__				):
      return sum(snake_case__				) / len(snake_case__				)


if __name__ == "__main__":
       # process id's
       __A   :							List[str]     	=						[1, 2, 3]

       # ensure that we actually have processes
       if len(processes) == 0:
              print('Zero amount of processes')
              raise SystemExit(0)

       # duration time of all processes
       __A   :							List[Any]     	=						[1_9, 8, 9]

       # ensure we can match each id to a duration time
       if len(duration_times) != len(processes):
              print('Unable to match all id\'s with their duration time')
              raise SystemExit(0)

       # get the waiting times and the turnaround times
       __A   :							Dict     	=						calculate_waiting_times(duration_times)
       __A   :							Optional[int]     	=						calculate_turnaround_times(duration_times, waiting_times)

       # get the average times
       __A   :							Dict     	=						calculate_average_waiting_time(waiting_times)
       __A   :							Tuple     	=						calculate_average_turnaround_time(turnaround_times)

       # print all the results
       print('Process ID\tDuration Time\tWaiting Time\tTurnaround Time')
       for i, process in enumerate(processes):
              print(
                  F"""{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"""
                  F"""{turnaround_times[i]}"""
              )
       print(F"""Average waiting time = {average_waiting_time}""")
       print(F"""Average turn around time = {average_turnaround_time}""")


