



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




def    UpperCAmelCase					(  lowercase__						:					Optional[int]	):



			'''simple docstring'''





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

			def white_space_fix(lowercase__						:					Optional[int]	):
						return " ".join(text.split()	)

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

			def lower(lowercase__						:					List[str]	):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(lowercase__	)	)	)	)




def    UpperCAmelCase					(  lowercase__						:					int				,					lowercase__						:					List[str]	):



			'''simple docstring'''





			a__					=  normalize_answer(lowercase__	).split()
			a__					=  normalize_answer(lowercase__	).split()
			a__					=  Counter(lowercase__	) & Counter(lowercase__	)
			a__					=  sum(common.values()	)
			if num_same == 0:
						return 0
			a__					=  1.0 * num_same / len(lowercase__	)
			a__					=  1.0 * num_same / len(lowercase__	)
			a__					=  (2 * precision * recall) / (precision + recall)
			return fa




def    UpperCAmelCase					(  lowercase__						:					Tuple				,					lowercase__						:					Dict	):



			'''simple docstring'''





			return normalize_answer(lowercase__	) == normalize_answer(lowercase__	)




def    UpperCAmelCase					(  lowercase__						:					Optional[Any]				,					lowercase__						:					int				,					lowercase__						:					List[str]	):



			'''simple docstring'''





			a__					=  []
			for ground_truth in ground_truths:
						a__					=  metric_fn(lowercase__				,					lowercase__	)
						scores_for_ground_truths.append(lowercase__	)
			return max(lowercase__	)




def    UpperCAmelCase					(  lowercase__						:					Dict				,					lowercase__						:					int	):



			'''simple docstring'''





			a__					=  a__					=  a__					=  0
			for article in dataset:
						for paragraph in article["paragraphs"]:
									for qa in paragraph["qas"]:
												total += 1
												if qa["id"] not in predictions:
															a__					=  """Unanswered question """ + qa["""id"""] + """ will receive score 0."""
															print(lowercase__				,					file=sys.stderr	)
															continue
												a__					=  [x["""text"""] for x in qa["""answers"""]]
												a__					=  predictions[qa["""id"""]]
												exact_match += metric_max_over_ground_truths(lowercase__				,					lowercase__				,					lowercase__	)
												fa += metric_max_over_ground_truths(lowercase__				,					lowercase__				,					lowercase__	)

			a__					=  100.0 * exact_match / total
			a__					=  100.0 * fa / total

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


if __name__ == "__main__":
	_lowercase				:					List[Any]     					="""1.1"""
	_lowercase				:					List[str]     					=argparse.ArgumentParser(description="""Evaluation for SQuAD """ + expected_version)
	parser.add_argument("""dataset_file""", help="""Dataset file""")
	parser.add_argument("""prediction_file""", help="""Prediction File""")
	_lowercase				:					Optional[int]     					=parser.parse_args()
	with open(args.dataset_file) as dataset_file:
		_lowercase				:					Union[str, Any]     					=json.load(dataset_file)
		if dataset_json["version"] != expected_version:
			print(
			    """Evaluation expects v-""" + expected_version + """, but got dataset with v-""" + dataset_json["""version"""],
			    file=sys.stderr,
			)
		_lowercase				:					int     					=dataset_json["""data"""]
	with open(args.prediction_file) as prediction_file:
		_lowercase				:					Any     					=json.load(prediction_file)
	print(json.dumps(evaluate(dataset, predictions)))






from .prime_check import Test

Test()

