



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

def 			a		( _UpperCAmelCase				:   List[str]		):







		'''simple docstring'''





		def remove_articles(_UpperCAmelCase				:   str		):
				return re.sub(R'''\b(a|an|the)\b'''					,  ''' '''					,  _UpperCAmelCase		)

		def white_space_fix(_UpperCAmelCase				:   Union[str, Any]		):
				return " ".join(text.split()		)

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

		def lower(_UpperCAmelCase				:   int		):
				return text.lower()

		return white_space_fix(remove_articles(remove_punc(lower(_UpperCAmelCase		)		)		)		)

def 			a		( _UpperCAmelCase				:   List[str]					,  _UpperCAmelCase				:   Union[str, Any]		):







		'''simple docstring'''





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

def 			a		( _UpperCAmelCase				:   Union[str, Any]					,  _UpperCAmelCase				:   Union[str, Any]		):







		'''simple docstring'''





		return normalize_answer(_UpperCAmelCase		) == normalize_answer(_UpperCAmelCase		)

def 			a		( _UpperCAmelCase				:   str					,  _UpperCAmelCase				:   List[Any]					,  _UpperCAmelCase				:   Union[str, Any]		):







		'''simple docstring'''





		__UpperCAmelCase       :	List[Any]						=      []
		for ground_truth in ground_truths:
				__UpperCAmelCase       :	List[Any]						=      metric_fn(_UpperCAmelCase					,  _UpperCAmelCase		)
				scores_for_ground_truths.append(_UpperCAmelCase		)
		return max(_UpperCAmelCase		)

def 			a		( _UpperCAmelCase				:   int					,  _UpperCAmelCase				:   Optional[int]		):







		'''simple docstring'''





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

						__UpperCAmelCase       :	List[Any]						=      [x['''text'''] for x in qa['''answers''']]
						__UpperCAmelCase       :	Optional[int]						=      predictions[qa['''id''']]

						__UpperCAmelCase       :	Tuple						=      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       :	Optional[Any]						=      exact_match / total
		__UpperCAmelCase       :	List[Any]						=      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     ,     __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)










import json
import logging
import os
import subprocess
from argparse import ArgumentParser


__A       =logging.getLogger(__name__)

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	Dict						=      ArgumentParser()
		__UpperCAmelCase				,   __UpperCAmelCase       :	int						=      parser.parse_known_args()
		for arg in unknown:
				if arg.startswith(('''-''', '''--''')		):
						parser.add_argument(arg.split('''='''		)[0]		)

		return parser.parse_args()

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	List[Any]						=      parse_args()
		__UpperCAmelCase       :	Tuple						=      88_88
		__UpperCAmelCase       :	Any						=      int(os.environ['''SM_NUM_GPUS''']		)
		__UpperCAmelCase       :	Optional[int]						=      json.loads(os.environ['''SM_HOSTS''']		)
		__UpperCAmelCase       :	int						=      len(_UpperCAmelCase		)
		__UpperCAmelCase       :	Optional[Any]						=      os.environ['''SM_CURRENT_HOST''']
		__UpperCAmelCase       :	List[str]						=      hosts.index(_UpperCAmelCase		)
		__UpperCAmelCase       :	Dict						=      '''INFO'''

		if num_nodes > 1:
				__UpperCAmelCase       :	Tuple						=      f'python -m torch.distributed.launch \\n                --nnodes={num_nodes}  \\n                --node_rank={rank}  \\n                --nproc_per_node={num_gpus}  \\n                --master_addr={hosts[0]}  \\n                --master_port={port} \\n                ./run_glue.py \\n                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]		)}'
		else:
				__UpperCAmelCase       :	Dict						=      f'python -m torch.distributed.launch \\n            --nproc_per_node={num_gpus}  \\n            ./run_glue.py \\n            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]		)}'
		try:
				subprocess.run(_UpperCAmelCase					,  shell=_UpperCAmelCase		)
		except Exception as e:
				logger.info(_UpperCAmelCase		)


if __name__ == "__main__":
				main()





