





'''simple docstring'''






import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


__UpperCamelCase      :   List[str]								=     100000000000
__UpperCamelCase      :   Tuple								=     10000

__UpperCamelCase							,  __UpperCamelCase      :   str								=     os.path.split(__file__)
__UpperCamelCase      :   List[str]								=     os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))





def      lowercase  (  lowerCAmelCase :						int   , lowerCAmelCase :						int):



				"""simple docstring"""


				_A :				Optional[Any]				=  pa.Table.from_pydict({'''col''': [0] * chunk_size})
				_A :				Union[str, Any]				=  pa.concat_tables([table] * (num_examples // chunk_size))
				return datasets.Dataset(lowerCAmelCase   , fingerprint='''table_100B''')



@dataclass
class  lowerCamelCase__				:


	"""simple docstring"""





	__magic_name__							   =		42
	__magic_name__							   =		42
	__magic_name__							   =		42
	__magic_name__							   =		42




	def 				_lowerCamelCase				(					self			)       ->				Any:
					_A :				Tuple				=  np.random.default_rng(self.seed			)
					_A :				Optional[int]				=  rng.integers(low=self.low   ,     high=self.high   ,     size=self.size			).tolist()




	def __iter__(					self			)       ->				Union[str, Any]:
					return iter(self._sampled_values			)




	def __len__(					self			)       ->				Dict:
					return self.size





@get_duration
def      lowercase  (  lowerCAmelCase :						datasets.Dataset):



				"""simple docstring"""


				_A :				Optional[int]				=  dataset[0]





@get_duration
def      lowercase  (  lowerCAmelCase :						datasets.Dataset):



				"""simple docstring"""


				_A :				str				=  dataset[-1]





@get_duration
def      lowercase  (  lowerCAmelCase :						datasets.Dataset):



				"""simple docstring"""


				_A :				List[Any]				=  dataset[range(len(lowerCAmelCase) // 2   , len(lowerCAmelCase) // 2 + 1024)]





@get_duration
def      lowercase  (  lowerCAmelCase :						datasets.Dataset):



				"""simple docstring"""


				_A :				Optional[Any]				=  dataset[RandIter(0   , len(lowerCAmelCase)   , 1024   , seed=42)]





def      lowercase  (  ):



				"""simple docstring"""


				_A :				Union[str, Any]				=  {'''num examples''': SPEED_TEST_N_EXAMPLES}
				_A :				Any				=  (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
				print('''generating dataset''')
				_A :				int				=  generate_100B_dataset(num_examples=lowerCAmelCase   , chunk_size=lowerCAmelCase)
				print('''Functions''')
				for func in functions:
								print(func.__name__)
								_A :				Optional[int]				=  func(lowerCAmelCase)

				with open(lowerCAmelCase   , '''wb''') as f:
								f.write(json.dumps(lowerCAmelCase).encode('''utf-8'''))


if __name__ == "__main__":  # useful to run the profiler
	benchmark_table_100B()







'''simple docstring'''






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





def      lowercase  (  lowerCAmelCase :						int):



				"""simple docstring"""


				def remove_articles(lowerCAmelCase :						List[Any]):
								return re.sub(R'''\b(a|an|the)\b'''   , ''' '''   , lowerCAmelCase)

				def white_space_fix(lowerCAmelCase :						Tuple):
								return " ".join(text.split())

				def remove_punc(lowerCAmelCase :						str):
								_A :				List[Any]				=  set(string.punctuation)
								return "".join(ch for ch in text if ch not in exclude)

				def lower(lowerCAmelCase :						Any):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase))))





def      lowercase  (  lowerCAmelCase :						int   , lowerCAmelCase :						Dict):



				"""simple docstring"""


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





def      lowercase  (  lowerCAmelCase :						Any   , lowerCAmelCase :						str):



				"""simple docstring"""


				return normalize_answer(lowerCAmelCase) == normalize_answer(lowerCAmelCase)





def      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						Optional[int]   , lowerCAmelCase :						Optional[int]):



				"""simple docstring"""


				_A :				Optional[int]				=  []
				for ground_truth in ground_truths:
								_A :				Union[str, Any]				=  metric_fn(lowerCAmelCase   , lowerCAmelCase)
								scores_for_ground_truths.append(lowerCAmelCase)
				return max(lowerCAmelCase)





def      lowercase  (  lowerCAmelCase :						int   , lowerCAmelCase :						str):



				"""simple docstring"""


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

												_A :				Optional[int]				=  [x['''text'''] for x in qa['''answers''']]
												_A :				Union[str, Any]				=  predictions[qa['''id''']]

												_A :				Optional[Any]				=  metric_max_over_ground_truths(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)
												if int(_exact_match) == 1:
																correct_ids.append(qa['''id'''])
												exact_match += _exact_match

												fa += metric_max_over_ground_truths(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)

				_A :				Dict				=  exact_match / total
				_A :				Dict				=  fa / total

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


if __name__ == "__main__":
	__UpperCamelCase      :   List[str]								=     '''1.0'''
	__UpperCamelCase      :   Union[str, Any]								=     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      :   Optional[int]								=     parser.parse_args()

	with open(args.data_file) as data_file:
		__UpperCamelCase      :   Optional[int]								=     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      :   Optional[Any]								=     dataset_json['''data''']

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

	__UpperCamelCase							,  __UpperCamelCase      :   List[str]								=     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)
