

'''simple docstring'''


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







def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     List[str]:

	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	):
		UpperCAmelCase_		:   Optional[int]													=       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     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase	)     ->     int:
	UpperCAmelCase_		:   int													=       normalize_answer(UpperCamelCase	).split()
	UpperCAmelCase_		:   int													=       normalize_answer(UpperCamelCase	).split()
	UpperCAmelCase_		:   int													=       Counter(UpperCamelCase	) & Counter(UpperCamelCase	)
	UpperCAmelCase_		:   List[str]													=       sum(common.values()	)
	if num_same == 0:
		return 0
	UpperCAmelCase_		:   List[Any]													=       1.0 * num_same / len(UpperCamelCase	)
	UpperCAmelCase_		:   Optional[int]													=       1.0 * num_same / len(UpperCamelCase	)
	UpperCAmelCase_		:   Dict													=       (2 * precision * recall) / (precision + recall)
	return fa







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase	)     ->     List[str]:
	return normalize_answer(UpperCamelCase	) == normalize_answer(UpperCamelCase	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     Dict:
	UpperCAmelCase_		:   Union[str, Any]													=       []
	for ground_truth in ground_truths:
		UpperCAmelCase_		:   Optional[int]													=       metric_fn(UpperCamelCase ,UpperCamelCase	)
		scores_for_ground_truths.append(UpperCamelCase	)
	return max(UpperCamelCase	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase	)     ->     Dict:
	UpperCAmelCase_		:   Dict													=       0
	UpperCAmelCase_		:   List[Any]													=       []
	for passage in dataset:
		for qa in passage["qas"]:
			total += 1
			if qa["id"] not in predictions:
				UpperCAmelCase_		:   Optional[Any]													=       f"""Unanswered question {qa["id"]} will receive score 0."""
				print(UpperCamelCase ,file=sys.stderr	)
				continue

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

			UpperCAmelCase_		:   Dict													=       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_		:   str													=       exact_match / total
	UpperCAmelCase_		:   Any													=       fa / total

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


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

						with open(args.data_file) as data_file:
												lowerCAmelCase__      				=			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__      				=			dataset_json["data"]

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

						lowerCAmelCase__				,  lowerCAmelCase__      				=			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 itertools import combinations, product







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     bool:
	if xa == ya == 0 or xa == ya == 0:
		return False
	UpperCAmelCase_		:   int													=       xa * xa + ya * ya
	UpperCAmelCase_		:   List[str]													=       xa * xa + ya * ya
	UpperCAmelCase_		:   Optional[Any]													=       (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
	return (
	    a_square + b_square == c_square
	    or a_square + c_square == b_square
	    or b_square + c_square == a_square
	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase = 5_0	)     ->     int:
	return sum(
	    1
	    for pta, pta in combinations(product(range(limit + 1	) ,repeat=2	) ,2	)
	    if is_right(*UpperCamelCase ,*UpperCamelCase	)	)


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






