code: 
  '''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)
  
  
  
  
  
  
  
style_context: 
  '''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
  
  
  
  
  
  
  