
'''simple docstring'''



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





def  _A		(    lowercase__	):







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

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

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

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

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





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





def  _A		(    lowercase__		,		lowercase__	):
						return normalize_answer(lowercase__	) == normalize_answer(lowercase__	)





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





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

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

																		lowercase__							 =       metric_max_over_ground_truths(lowercase__		,		lowercase__		,		lowercase__	)
																		if int(_exact_match	) == 1:
																								correct_ids.append(qa["""id"""]	)
																		exact_match += _exact_match

																		fa += metric_max_over_ground_truths(lowercase__		,		lowercase__		,		lowercase__	)

						lowercase__							 =       exact_match / total
						lowercase__							 =       fa / total

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


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

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

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

				__A          =    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 bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger





def  _A		(							lowercase__		,		lowercase__	):
						lowercase__							 =       copy.deepcopy(lowercase__	)
						lowercase__							 =       sorted(lowercase__	)
						lowercase__							 =       [bisect.bisect_right(lowercase__		,		lowercase__	) for y in x]
						return quantized





def  _A		(							lowercase__		,		lowercase__=0	):
						lowercase__							 =       np.arange(start=3		,		stop=lowercase__		,		step=4	).tolist() if k > 0 else [10]
						lowercase__							 =       _quantize(lowercase__		,		lowercase__	)
						# count number of elements per group
						lowercase__							 =       np.unique(lowercase__		,		return_counts=lowercase__	)[1]
						lowercase__							 =       [0] + bins + [np.inf]
						logger.info("""Using {} as bins for aspect lengths quantization""".format(lowercase__	)	)
						logger.info("""Count of instances per bin: {}""".format(lowercase__	)	)
						return groups







class 			A							(       __UpperCAmelCase				):



			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[int]:




									'''simple docstring'''



									if not isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
															raise ValueError(
															    """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(lowerCamelCase__       )       )
									lowercase__							 =       sampler
									lowercase__							 =       group_ids
									lowercase__							 =       batch_size



			def __iter__(					self       )			->		List[str]:




									'''simple docstring'''



									lowercase__							 =       defaultdict(lowerCamelCase__       )
									lowercase__							 =       defaultdict(lowerCamelCase__       )

									lowercase__							 =       0
									for idx in self.sampler:
															lowercase__							 =       self.group_ids[idx]
															buffer_per_group[group_id].append(lowerCamelCase__       )
															samples_per_group[group_id].append(lowerCamelCase__       )
															if len(buffer_per_group[group_id]       ) == self.batch_size:
																					yield buffer_per_group[group_id]  # TODO
																					num_batches += 1
																					del buffer_per_group[group_id]
															assert len(buffer_per_group[group_id]       ) < self.batch_size

									# now we have run out of elements that satisfy
									# the group criteria, let's return the remaining
									# elements so that the size of the sampler is
									# deterministic
									lowercase__							 =       len(self       )
									lowercase__							 =       expected_num_batches - num_batches
									if num_remaining > 0:
															# for the remaining batches, group the batches by similar lengths
															lowercase__							 =       []
															for group_id, idxs in sorted(buffer_per_group.items()       ,     key=lambda lowerCamelCase__       : x[0]       ):
																					batch_idx.extend(lowerCamelCase__       )
																					if len(lowerCamelCase__       ) >= self.batch_size:
																											yield batch_idx[: self.batch_size]
																											lowercase__							 =       batch_idx[self.batch_size :]
																											num_remaining -= 1
															if len(lowerCamelCase__       ) > 0:
																					yield batch_idx
																					num_remaining -= 1
									assert num_remaining == 0



			def __len__(					self       )			->		Any:




									'''simple docstring'''



									return (len(self.sampler       ) + self.batch_size - 1) // self.batch_size






