




"""simple docstring"""





def 						lowerCAmelCase							(	lowerCamelCase = 10**6						):
						'''simple docstring'''


						if n <= 0:
												raise ValueError('''Please enter an integer greater than 0'''						)

						snake_case__    :   Dict							     =   list(range(n + 1						)						)
						for number in range(2 ,     n + 1						):
												if phi[number] == number:
																		phi[number] -= 1
																		for multiple in range(number * 2 ,     n + 1 ,     __A						):
																								snake_case__    :   Dict							     =   (phi[multiple] // number) * (number - 1)

						snake_case__    :   Union[str, Any]							     =   1
						for number in range(1 ,     n + 1						):
												if (answer / phi[answer]) < (number / phi[number]):
																		snake_case__    :   Union[str, Any]							     =   number

						return answer


if __name__ == "__main__":
							print(solution())






"""simple docstring"""





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

def 						lowerCAmelCase							(	lowerCamelCase						):
						'''simple docstring'''


						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						):
												snake_case__    :   Tuple							     =   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 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


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

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						return normalize_answer(lowerCamelCase						) == normalize_answer(lowerCamelCase						)

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   int							     =   []
						for ground_truth in ground_truths:
												snake_case__    :   Optional[Any]							     =   metric_fn(lowerCamelCase ,     lowerCamelCase						)
												scores_for_ground_truths.append(lowerCamelCase						)
						return max(lowerCamelCase						)

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


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

																		snake_case__    :   Any							     =   [x['''text'''] for x in qa['''answers''']]
																		snake_case__    :   Dict							     =   predictions[qa['''id''']]

																		snake_case__    :   Union[str, Any]							     =   metric_max_over_ground_truths(lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						)
																		if int(_exact_match						) == 1:
																								correct_ids.append(qa['''id''']						)
																		exact_match += _exact_match

																		fa += metric_max_over_ground_truths(lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						)

						snake_case__    :   str							     =   exact_match / total
						snake_case__    :   Optional[int]							     =   fa / total

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


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

							with open(args.data_file) as data_file:
														_lowercase		 =      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,
																					)
														_lowercase		 =      dataset_json["""data"""]

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

							_lowercase ,_lowercase		 =      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)


