



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



def 				_lowerCAmelCase      (_lowerCAmelCase):




					def remove_articles(_lowerCAmelCase):
										return re.sub(r"\b(a|an|the)\b"				,		" "				,		_UpperCamelCase)

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

					def remove_punc(_lowerCAmelCase):
										UpperCamelCase_											=				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(_UpperCamelCase))))



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase):
					UpperCamelCase_											=				normalize_answer(_UpperCamelCase).split()
					UpperCamelCase_											=				normalize_answer(_UpperCamelCase).split()
					UpperCamelCase_											=				Counter(_UpperCamelCase) & Counter(_UpperCamelCase)
					UpperCamelCase_											=				sum(common.values())
					if num_same == 0:
										return 0
					UpperCamelCase_											=				1.0 * num_same / len(_UpperCamelCase)
					UpperCamelCase_											=				1.0 * num_same / len(_UpperCamelCase)
					UpperCamelCase_											=				(2 * precision * recall) / (precision + recall)
					return fa



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase):
					return normalize_answer(_UpperCamelCase) == normalize_answer(_UpperCamelCase)



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase):
					UpperCamelCase_											=				[]
					for ground_truth in ground_truths:
										UpperCamelCase_											=				metric_fn(_UpperCamelCase				,		_UpperCamelCase)
										scores_for_ground_truths.append(_UpperCamelCase)
					return max(_UpperCamelCase)



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

															UpperCamelCase_											=				[x["text"] for x in qa["answers"]]
															UpperCamelCase_											=				predictions[qa["id"]]

															UpperCamelCase_											=				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)

					UpperCamelCase_											=				exact_match / total
					UpperCamelCase_											=				fa / total

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


if __name__ == "__main__":
	UpperCAmelCase				:							List[Any]								="""1.0"""
	UpperCAmelCase				:							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.""")
	UpperCAmelCase				:							List[Any]								=parser.parse_args()

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

	with open(args.pred_file) as pred_file:
		UpperCAmelCase				:							List[str]								=json.load(pred_file)

	UpperCAmelCase	,	UpperCAmelCase				:							Union[str, Any]								=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)







from __future__ import annotations



def 				_lowerCAmelCase      (_lowerCAmelCase):
					UpperCamelCase_											=				{1}
					UpperCamelCase_											=				[1]
					for _ in range(2				,		depth + 1):
										UpperCamelCase_											=				[*previous_coefficients, 0]
										UpperCamelCase_											=				[0, *previous_coefficients]
										UpperCamelCase_											=				[]
										for x, y in zip(_lowerCAmelCase				,		_lowerCAmelCase):
															coefficients.add(x + y)
															previous_coefficients.append(x + y)
					return coefficients



def 				_lowerCAmelCase      (_lowerCAmelCase):
					UpperCamelCase_											=				set()
					for number in unique_coefficients:
										UpperCamelCase_											=				2
										UpperCamelCase_											=				number
										while divisor**2 <= copy_number:
															UpperCamelCase_											=				0
															while copy_number % divisor == 0:
																				copy_number //= divisor
																				multiplicity += 1
															if multiplicity >= 2:
																				non_squarefrees.add(_lowerCAmelCase)
																				break
															divisor += 1

					return unique_coefficients.difference(_lowerCAmelCase)



def 				_lowerCAmelCase      (_lowerCAmelCase = 51):
					UpperCamelCase_											=				get_pascal_triangle_unique_coefficients(_lowerCAmelCase)
					UpperCamelCase_											=				get_squarefrees(_lowerCAmelCase)
					return sum(_lowerCAmelCase)


if __name__ == "__main__":
	print(F"{solution() = }")
