


"""simple docstring"""
import numpy as np







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Any:
							snake_case_     							=		n_draws * class_counts / class_counts.sum()
							# floored means we don't overshoot n_samples, but probably undershoot
							snake_case_     							=		np.floor(_SCREAMING_SNAKE_CASE					)
							# we add samples according to how much "left over" probability
							# they had, until we arrive at n_samples
							snake_case_     							=		int(n_draws - floored.sum()					)
							if need_to_add > 0:
														snake_case_     							=		continuous - floored
														snake_case_     							=		np.sort(np.unique(_SCREAMING_SNAKE_CASE					)					)[::-1]
														# add according to remainder, but break ties
														# randomly to avoid biases
														for value in values:
																					((snake_case_)							,    )     							=		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
																					snake_case_     							=		min(len(_SCREAMING_SNAKE_CASE					)					,			_SCREAMING_SNAKE_CASE					)
																					snake_case_     							=		rng.choice(_SCREAMING_SNAKE_CASE					,			size=_SCREAMING_SNAKE_CASE					,			replace=_SCREAMING_SNAKE_CASE					)
																					floored[inds] += 1
																					need_to_add -= add_now
																					if need_to_add == 0:
																												break
							return floored.astype(np.intaa					)







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE=10					)     ->					int:
							snake_case_							,    snake_case_     							=		np.unique(_SCREAMING_SNAKE_CASE					,			return_inverse=_SCREAMING_SNAKE_CASE					)
							snake_case_     							=		classes.shape[0]
							snake_case_     							=		np.bincount(_SCREAMING_SNAKE_CASE					)
							if np.min(_SCREAMING_SNAKE_CASE					) < 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)					)
							snake_case_     							=		np.split(np.argsort(_SCREAMING_SNAKE_CASE					,			kind="""mergesort"""					)					,			np.cumsum(_SCREAMING_SNAKE_CASE					)[:-1]					)
							for _ in range(_SCREAMING_SNAKE_CASE					):
														snake_case_     							=		approximate_mode(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)
														snake_case_     							=		class_counts - n_i
														snake_case_     							=		approximate_mode(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)

														snake_case_     							=		[]
														snake_case_     							=		[]

														for i in range(_SCREAMING_SNAKE_CASE					):
																					snake_case_     							=		rng.permutation(class_counts[i]					)
																					snake_case_     							=		class_indices[i].take(_SCREAMING_SNAKE_CASE					,			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]]					)
														snake_case_     							=		rng.permutation(_SCREAMING_SNAKE_CASE					)
														snake_case_     							=		rng.permutation(_SCREAMING_SNAKE_CASE					)

														yield train, test







"""simple docstring"""
import argparse
import json
import re
import string
import sys
from collections import Counter







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					Any:

							def remove_articles(_SCREAMING_SNAKE_CASE					):
														return re.sub(r"""\b(a|an|the)\b"""					,			""" """					,			_SCREAMING_SNAKE_CASE					)

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

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

							def lower(_SCREAMING_SNAKE_CASE					):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(_SCREAMING_SNAKE_CASE					)					)					)					)







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Optional[int]:
							snake_case_     							=		normalize_answer(_SCREAMING_SNAKE_CASE					).split()
							snake_case_     							=		normalize_answer(_SCREAMING_SNAKE_CASE					).split()
							snake_case_     							=		Counter(_SCREAMING_SNAKE_CASE					) & Counter(_SCREAMING_SNAKE_CASE					)
							snake_case_     							=		sum(common.values()					)
							if num_same == 0:
														return 0
							snake_case_     							=		1.0 * num_same / len(_SCREAMING_SNAKE_CASE					)
							snake_case_     							=		1.0 * num_same / len(_SCREAMING_SNAKE_CASE					)
							snake_case_     							=		(2 * precision * recall) / (precision + recall)
							return fa







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Union[str, Any]:
							return normalize_answer(_SCREAMING_SNAKE_CASE					) == normalize_answer(_SCREAMING_SNAKE_CASE					)







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Optional[int]:
							snake_case_     							=		[]
							for ground_truth in ground_truths:
														snake_case_     							=		metric_fn(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)
														scores_for_ground_truths.append(_SCREAMING_SNAKE_CASE					)
							return max(_SCREAMING_SNAKE_CASE					)







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Optional[int]:
							snake_case_     							=		snake_case_     							=		snake_case_     							=		0
							for article in dataset:
														for paragraph in article["paragraphs"]:
																					for qa in paragraph["qas"]:
																												total += 1
																												if qa["id"] not in predictions:
																																			snake_case_     							=		"""Unanswered question """ + qa["""id"""] + """ will receive score 0."""
																																			print(_SCREAMING_SNAKE_CASE					,			file=sys.stderr					)
																																			continue
																												snake_case_     							=		[x["""text"""] for x in qa["""answers"""]]
																												snake_case_     							=		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					)

							snake_case_     							=		100.0 * exact_match / total
							snake_case_     							=		100.0 * fa / total

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


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE				:     Tuple		=     '1.1'
				__SCREAMING_SNAKE_CASE				:     Union[str, 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')
				__SCREAMING_SNAKE_CASE				:     Union[str, Any]		=     parser.parse_args()
				with open(args.dataset_file) as dataset_file:
								__SCREAMING_SNAKE_CASE				:     Optional[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,
												)
								__SCREAMING_SNAKE_CASE				:     str		=     dataset_json['data']
				with open(args.prediction_file) as prediction_file:
								__SCREAMING_SNAKE_CASE				:     int		=     json.load(prediction_file)
				print(json.dumps(evaluate(dataset, predictions)))



