




"""simple docstring"""




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







def       __UpperCAmelCase      (				__UpperCamelCase					):

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

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

			def remove_punc(__UpperCamelCase					):
						__lowercase	:       Any				=   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(__UpperCamelCase					)					)					)					)







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







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					):
			return normalize_answer(__UpperCamelCase					) == normalize_answer(__UpperCamelCase					)







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					):
			__lowercase	:       Any				=   []
			for ground_truth in ground_truths:
						__lowercase	:       Any				=   metric_fn(__UpperCamelCase					,							__UpperCamelCase					)
						scores_for_ground_truths.append(__UpperCamelCase					)
			return max(__UpperCamelCase					)







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

									__lowercase	:       List[Any]				=   [x['''text'''] for x in qa['''answers''']]
									__lowercase	:       Any				=   predictions[qa['''id''']]

									__lowercase	:       List[str]				=   metric_max_over_ground_truths(__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					)
									if int(_exact_match					) == 1:
												correct_ids.append(qa['''id''']					)
									exact_match += _exact_match

									fa += metric_max_over_ground_truths(__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					)

			__lowercase	:       Optional[int]				=   exact_match / total
			__lowercase	:       Tuple				=   fa / total

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


if __name__ == "__main__":
			UpperCamelCase            =   '1.0'
			UpperCamelCase            =   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.')
			UpperCamelCase            =   parser.parse_args()

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

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

			UpperCamelCase       ,	UpperCamelCase            =   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      (				__UpperCamelCase					):
			__lowercase	:       List[Any]				=   [0] * len(__UpperCamelCase					)
			for i in range(1					,							len(__UpperCamelCase					)					):
						__lowercase	:       Optional[Any]				=   duration_times[i - 1] + waiting_times[i - 1]
			return waiting_times







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					):
			return [
			    duration_time + waiting_times[i]
			    for i, duration_time in enumerate(__UpperCamelCase					)
			]







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return sum(__UpperCamelCase					) / len(__UpperCamelCase					)







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return sum(__UpperCamelCase					) / len(__UpperCamelCase					)


if __name__ == "__main__":
			# process id's
			a_            =   [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_            =   [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_            =   calculate_waiting_times(duration_times)
			a_            =   calculate_turnaround_times(duration_times, waiting_times)

			# get the average times
			a_            =   calculate_average_waiting_time(waiting_times)
			a_            =   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}")



