





class 	_UpperCamelCase	:







					'''simple docstring'''

					def __init__(    self     :	Any			,       __lowercase     :	int		):






						'''simple docstring'''







						UpperCAmelCase_		    =  data
						UpperCAmelCase_		    =  42
						UpperCAmelCase_		    =  42




def  A_(      A ):
	UpperCAmelCase_		    =  0
	UpperCAmelCase_		    =  x




def  A_(      A					,   A ):
	UpperCAmelCase_    ,						UpperCAmelCase_		    =  find_set(A ), find_set(A )
	if x == y:
		return

	elif x.rank > y.rank:
		UpperCAmelCase_		    =  x
	else:
		UpperCAmelCase_		    =  y
		if x.rank == y.rank:
			y.rank += 1




def  A_(      A ):
	if x != x.parent:
		UpperCAmelCase_		    =  find_set(x.parent )
	return x.parent




def  A_(      A ):
	UpperCAmelCase_		    =  ({0, 1, 2}, {3, 4, 5})
	for s in sets:
		if node.data in s:
			return s
	UpperCAmelCase_		    =  f"""{node.data} is not in {sets}"""
	raise ValueError(A )




def  A_(      ):
	UpperCAmelCase_		    =  [Node(A ) for i in range(6 )]
	for v in vertex:
		make_set(A )

	union_set(vertex[0]					,   vertex[1] )
	union_set(vertex[1]					,   vertex[2] )
	union_set(vertex[3]					,   vertex[4] )
	union_set(vertex[3]					,   vertex[5] )

	for nodea in vertex:
		for nodea in vertex:
			if find_python_set(A ).isdisjoint(find_python_set(A ) ):
				assert find_set(A ) != find_set(A )
			else:
				assert find_set(A ) == find_set(A )


if __name__ == "__main__":
				test_disjoint_set()









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




def  A_(      A ):

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

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

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

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

	return white_space_fix(remove_articles(remove_punc(lower(A ) ) ) )




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




def  A_(      A					,   A ):
	return normalize_answer(A ) == normalize_answer(A )




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  []
	for ground_truth in ground_truths:
		UpperCAmelCase_		    =  metric_fn(A					,   A )
		scores_for_ground_truths.append(A )
	return max(A )




def  A_(      A					,   A ):
	UpperCAmelCase_		    =  UpperCAmelCase_		    =  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(A					,   file=sys.stderr )
				continue

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

			UpperCAmelCase_		    =  metric_max_over_ground_truths(A					,   A					,   A )
			if int(_exact_match ) == 1:
				correct_ids.append(qa["""id"""] )
			exact_match += _exact_match

			fa += metric_max_over_ground_truths(A					,   A					,   A )

	UpperCAmelCase_		    =  exact_match / total
	UpperCAmelCase_		    =  fa / total

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


if __name__ == "__main__":
				UpperCamelCase__ :			Any       	=			"""1.0"""
				UpperCamelCase__ :			Optional[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__ :			Any       	=			parser.parse_args()

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

				with open(args.pred_file) as pred_file:
								UpperCamelCase__ :			List[Any]       	=			json.load(pred_file)

				UpperCamelCase__	,      UpperCamelCase__ :			Optional[int]       	=			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)


