





'''simple docstring'''







import unittest
import warnings

from datasets.utils import experimental





@experimental
def 							lowerCamelCase_   (	)			->						str:
						return "success"


class        __a   (unittest.TestCase  ):

							def      UpperCAmelCase__						(				self				:  List[Any] )       ->							Tuple:







													"""simple docstring"""
													with warnings.catch_warnings(record=__magic_name__ ) as w:
																			warnings.simplefilter('''always''' )
																			self.assertEqual(dummy_function()							,  '''success''' )
													self.assertEqual(len(__magic_name__ )							,  1 )












'''simple docstring'''







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





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Optional[Any] )			->						str:

						def remove_articles(SCREAMING_SNAKE_CASE__   :      Union[str, Any] ):
												return re.sub(R'''\b(a|an|the)\b''',      ''' ''',      SCREAMING_SNAKE_CASE__ )

						def white_space_fix(SCREAMING_SNAKE_CASE__   :      Dict ):
												return " ".join(text.split() )

						def remove_punc(SCREAMING_SNAKE_CASE__   :      Union[str, Any] ):
												UpperCAmelCase_      :   int      =	set(string.punctuation )
												return "".join(ch for ch in text if ch not in exclude )

						def lower(SCREAMING_SNAKE_CASE__   :      str ):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE__ ) ) ) )





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





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      int,      SCREAMING_SNAKE_CASE__   :      int )			->						Any:
						return normalize_answer(SCREAMING_SNAKE_CASE__ ) == normalize_answer(SCREAMING_SNAKE_CASE__ )





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





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      List[str],      SCREAMING_SNAKE_CASE__   :      Optional[Any] )			->						Any:
						UpperCAmelCase_      :   str      =	0
						for article in dataset:
												for paragraph in article["paragraphs"]:
																		for qa in paragraph["qas"]:
																								total += 1
																								if qa["id"] not in predictions:
																														UpperCAmelCase_      :   Union[str, Any]      =	'''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
																														print(SCREAMING_SNAKE_CASE__,      file=sys.stderr )
																														continue
																								UpperCAmelCase_      :   Optional[Any]      =	[x['''text'''] for x in qa['''answers''']]
																								UpperCAmelCase_      :   int      =	predictions[qa['''id''']]
																								exact_match += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
																								fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )

						UpperCAmelCase_      :   Any      =	1_00.0 * exact_match / total
						UpperCAmelCase_      :   Optional[int]      =	1_00.0 * fa / total

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


if __name__ == "__main__":
					snake_case_  :							Any            =	"1.1"
					snake_case_  :							Any            =	argparse.ArgumentParser(description="Evaluation for SQuAD " + expected_version)
					parser.add_argument("dataset_file", help="Dataset file")
					parser.add_argument("prediction_file", help="Prediction File")
					snake_case_  :							int            =	parser.parse_args()
					with open(args.dataset_file) as dataset_file:
										snake_case_  :							List[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,
															)
										snake_case_  :							Tuple            =	dataset_json["data"]
					with open(args.prediction_file) as prediction_file:
										snake_case_  :							Tuple            =	json.load(prediction_file)
					print(json.dumps(evaluate(dataset, predictions)))





