




"""simple docstring"""





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

def 		lowerCAmelCase				(			lowerCamelCase		):
						'''simple docstring'''


						def remove_articles(lowerCamelCase		):
												return re.sub(R'''\b(a|an|the)\b'''   ,      ''' '''   ,      SCREAMING_SNAKE_CASE__		)

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

						def remove_punc(lowerCamelCase		):
												snake_case__							:       str				      =       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(SCREAMING_SNAKE_CASE__		)		)		)		)

def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


						snake_case__							:       Any				      =       normalize_answer(SCREAMING_SNAKE_CASE__		).split()
						snake_case__							:       Any				      =       normalize_answer(SCREAMING_SNAKE_CASE__		).split()
						snake_case__							:       List[str]				      =       Counter(SCREAMING_SNAKE_CASE__		) & Counter(SCREAMING_SNAKE_CASE__		)
						snake_case__							:       int				      =       sum(common.values()		)
						if num_same == 0:
												return 0
						snake_case__							:       Any				      =       1.0 * num_same / len(SCREAMING_SNAKE_CASE__		)
						snake_case__							:       List[str]				      =       1.0 * num_same / len(SCREAMING_SNAKE_CASE__		)
						snake_case__							:       Tuple				      =       (2 * precision * recall) / (precision + recall)
						return fa

def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


						return normalize_answer(SCREAMING_SNAKE_CASE__		) == normalize_answer(SCREAMING_SNAKE_CASE__		)

def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


						snake_case__							:       Any				      =       []
						for ground_truth in ground_truths:
												snake_case__							:       Tuple				      =       metric_fn(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__		)
												scores_for_ground_truths.append(SCREAMING_SNAKE_CASE__		)
						return max(SCREAMING_SNAKE_CASE__		)

def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


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

																		snake_case__							:       List[Any]				      =       [x['''text'''] for x in qa['''answers''']]
																		snake_case__							:       Optional[int]				      =       predictions[qa['''id''']]

																		snake_case__							:       List[Any]				      =       metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__		)
																		if int(_exact_match		) == 1:
																								correct_ids.append(qa['''id''']		)
																		exact_match += _exact_match

																		fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__		)

						snake_case__							:       Optional[Any]				      =       exact_match / total
						snake_case__							:       Tuple				      =       fa / total

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


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

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

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

							_lowercase   ,_lowercase						   =     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)






def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Union[str, Any]     ,	SCREAMING_SNAKE_CASE__     :   int							):
     if n < 0:
          return 0
     # table[i] represents the number of ways to get to amount i
     __UpperCamelCase       				=[0] * (n + 1)

     # There is exactly 1 way to get to zero(You pick no coins).
     __UpperCamelCase       				=1

     # Pick all coins one by one and update table[] values
     # after the index greater than or equal to the value of the
     # picked coin
     for coin_val in s:
          for j in range(SCREAMING_SNAKE_CASE__     ,	n + 1							):
               table[j] += table[j - coin_val]

     return table[n]


if __name__ == "__main__":
   import doctest

   doctest.testmod()





