"""simple docstring"""







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


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

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

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

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

						def lower(UpperCAmelCase__							:      Union[str, Any]      ):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase__      )      )      )      )


def 	a__   (		UpperCAmelCase__							:      str		,      UpperCAmelCase__							:      Dict      )->						Any:
						UpperCAmelCase__       						=		normalize_answer(UpperCAmelCase__      ).split()
						UpperCAmelCase__       						=		normalize_answer(UpperCAmelCase__      ).split()
						UpperCAmelCase__       						=		Counter(UpperCAmelCase__      ) & Counter(UpperCAmelCase__      )
						UpperCAmelCase__       						=		sum(common.values()      )
						if num_same == 0:
												return 0
						UpperCAmelCase__       						=		1.0 * num_same / len(UpperCAmelCase__      )
						UpperCAmelCase__       						=		1.0 * num_same / len(UpperCAmelCase__      )
						UpperCAmelCase__       						=		(2 * precision * recall) / (precision + recall)
						return fa


def 	a__   (		UpperCAmelCase__							:      Dict		,      UpperCAmelCase__							:      int      )->						Optional[Any]:
						return normalize_answer(UpperCAmelCase__      ) == normalize_answer(UpperCAmelCase__      )


def 	a__   (		UpperCAmelCase__							:      List[Any]		,      UpperCAmelCase__							:      Dict		,      UpperCAmelCase__							:      Dict      )->						Optional[Any]:
						UpperCAmelCase__       						=		[]
						for ground_truth in ground_truths:
												UpperCAmelCase__       						=		metric_fn(UpperCAmelCase__		,      UpperCAmelCase__      )
												scores_for_ground_truths.append(UpperCAmelCase__      )
						return max(UpperCAmelCase__      )


def 	a__   (		UpperCAmelCase__							:      List[str]		,      UpperCAmelCase__							:      Tuple      )->						Union[str, Any]:
						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(UpperCAmelCase__		,      file=sys.stderr      )
																								continue

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

																		UpperCAmelCase__       						=		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__       						=		exact_match / total
						UpperCAmelCase__       						=		fa / total

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


if __name__ == "__main__":
						UpperCamelCase_							:							List[Any]				 =		"""1.0"""
						UpperCamelCase_							:							int				 =		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_							:							str				 =		parser.parse_args()

						with open(args.data_file) as data_file:
												UpperCamelCase_							:							Optional[Any]				 =		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_							:							Any				 =		dataset_json["""data"""]

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

						UpperCamelCase_ ,    UpperCamelCase_							:							Union[str, Any]				 =		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"""







import numpy as np


def 	a__   (		UpperCAmelCase__							:      List[str]		,      UpperCAmelCase__							:      int		,      UpperCAmelCase__							:      str      )->						Optional[int]:
						UpperCAmelCase__       						=		n_draws * class_counts / class_counts.sum()
						# floored means we don't overshoot n_samples, but probably undershoot
						UpperCAmelCase__       						=		np.floor(UpperCAmelCase__      )
						# we add samples according to how much "left over" probability
						# they had, until we arrive at n_samples
						UpperCAmelCase__       						=		int(n_draws - floored.sum()      )
						if need_to_add > 0:
												UpperCAmelCase__       						=		continuous - floored
												UpperCAmelCase__       						=		np.sort(np.unique(UpperCAmelCase__      )      )[::-1]
												# add according to remainder, but break ties
												# randomly to avoid biases
												for value in values:
																		((UpperCAmelCase__)						,   )       						=		np.where(remainder == value      )
																		# if we need_to_add less than what's in inds
																		# we draw randomly from them.
																		# if we need to add more, we add them all and
																		# go to the next value
																		UpperCAmelCase__       						=		min(len(UpperCAmelCase__      )		,      UpperCAmelCase__      )
																		UpperCAmelCase__       						=		rng.choice(UpperCAmelCase__		,      size=UpperCAmelCase__		,      replace=UpperCAmelCase__      )
																		floored[inds] += 1
																		need_to_add -= add_now
																		if need_to_add == 0:
																								break
						return floored.astype(np.intaa      )







def 	a__   (		UpperCAmelCase__							:      Optional[int]		,      UpperCAmelCase__							:      Optional[int]		,      UpperCAmelCase__							:      Dict		,      UpperCAmelCase__							:      Any		,      UpperCAmelCase__							:      Union[str, Any]=1_0      )->						Tuple:
						UpperCAmelCase__						,   UpperCAmelCase__       						=		np.unique(UpperCAmelCase__		,      return_inverse=UpperCAmelCase__      )
						UpperCAmelCase__       						=		classes.shape[0]
						UpperCAmelCase__       						=		np.bincount(UpperCAmelCase__      )
						if np.min(UpperCAmelCase__      ) < 2:
												raise ValueError("""Minimum class count error"""      )
						if n_train < n_classes:
												raise ValueError(
												    """The train_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_train, n_classes)      )
						if n_test < n_classes:
												raise ValueError(
												    """The test_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_test, n_classes)      )
						UpperCAmelCase__       						=		np.split(np.argsort(UpperCAmelCase__		,      kind="""mergesort"""      )		,      np.cumsum(UpperCAmelCase__      )[:-1]      )
						for _ in range(UpperCAmelCase__      ):
												UpperCAmelCase__       						=		approximate_mode(UpperCAmelCase__		,      UpperCAmelCase__		,      UpperCAmelCase__      )
												UpperCAmelCase__       						=		class_counts - n_i
												UpperCAmelCase__       						=		approximate_mode(UpperCAmelCase__		,      UpperCAmelCase__		,      UpperCAmelCase__      )

												UpperCAmelCase__       						=		[]
												UpperCAmelCase__       						=		[]

												for i in range(UpperCAmelCase__      ):
																		UpperCAmelCase__       						=		rng.permutation(class_counts[i]      )
																		UpperCAmelCase__       						=		class_indices[i].take(UpperCAmelCase__		,      mode="""clip"""      )
																		train.extend(perm_indices_class_i[: n_i[i]]      )
																		test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]      )
												UpperCAmelCase__       						=		rng.permutation(UpperCAmelCase__      )
												UpperCAmelCase__       						=		rng.permutation(UpperCAmelCase__      )

												yield train, test

