






"""simple docstring"""





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



def      __lowercase       (     a			:					Optional[int]			)       ->       Union[str, Any]:






      def remove_articles(a			:					Optional[int]			):
            return re.sub(R'''\b(a|an|the)\b'''							,		''' '''							,		_UpperCAmelCase			)

      def white_space_fix(a			:					Dict			):
            return " ".join(text.split()			)

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

      def lower(a			:					Tuple			):
            return text.lower()

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



def      __lowercase       (     a			:					Optional[int]							,		a			:					Dict			)       ->       Any:
      __snake_case   :     Dict	      =normalize_answer(_UpperCAmelCase			).split()
      __snake_case   :     Dict	      =normalize_answer(_UpperCAmelCase			).split()
      __snake_case   :     Optional[Any]	      =Counter(_UpperCAmelCase			) & Counter(_UpperCAmelCase			)
      __snake_case   :     List[Any]	      =sum(common.values()			)
      if num_same == 0:
            return 0
      __snake_case   :     str	      =1.0 * num_same / len(_UpperCAmelCase			)
      __snake_case   :     Any	      =1.0 * num_same / len(_UpperCAmelCase			)
      __snake_case   :     List[str]	      =(2 * precision * recall) / (precision + recall)
      return fa



def      __lowercase       (     a			:					Any							,		a			:					str			)       ->       Optional[Any]:
      return normalize_answer(_UpperCAmelCase			) == normalize_answer(_UpperCAmelCase			)



def      __lowercase       (     a			:					List[str]							,		a			:					List[str]							,		a			:					Union[str, Any]			)       ->       Any:
      __snake_case   :     List[str]	      =[]
      for ground_truth in ground_truths:
            __snake_case   :     Union[str, Any]	      =metric_fn(_UpperCAmelCase							,		_UpperCAmelCase			)
            scores_for_ground_truths.append(_UpperCAmelCase			)
      return max(_UpperCAmelCase			)



def      __lowercase       (     a			:					Optional[int]							,		a			:					int			)       ->       int:
      __snake_case   :     Optional[Any]	      =0
      __snake_case   :     int	      =[]
      for passage in dataset:
            for qa in passage["qas"]:
                  total += 1
                  if qa["id"] not in predictions:
                        __snake_case   :     List[str]	      =f'''Unanswered question {qa["id"]} will receive score 0.'''
                        print(_UpperCAmelCase							,		file=sys.stderr			)
                        continue

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

                  __snake_case   :     int	      =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			)

      __snake_case   :     Optional[Any]	      =exact_match / total
      __snake_case   :     Optional[Any]	      =fa / total

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


if __name__ == "__main__":
    UpperCamelCase_						:					Optional[int]      				=			"""1.0"""
    UpperCamelCase_						:					List[str]      				=			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_						:					Dict      				=			parser.parse_args()

    with open(args.data_file) as data_file:
        UpperCamelCase_						:					Tuple      				=			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_						:					Any      				=			dataset_json["""data"""]

    with open(args.pred_file) as pred_file:
        UpperCamelCase_						:					List[str]      				=			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)







def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					for index, item in enumerate(_UpperCAmelCase				):
										if item == target:
															return index
					return -1





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					if not (0 <= high < len(_UpperCAmelCase				) and 0 <= low < len(_UpperCAmelCase				)):
										raise Exception('Invalid upper or lower bound!'				)
					if high < low:
										return -1
					if sequence[low] == target:
										return low
					if sequence[high] == target:
										return high
					return rec_linear_search(_UpperCAmelCase       ,			low + 1       ,			high - 1       ,			_UpperCAmelCase				)


if __name__ == "__main__":
		__magic_name__       =     input('''Enter numbers separated by comma:\n''').strip()
		__magic_name__       =     [int(item.strip()) for item in user_input.split(''',''')]

		__magic_name__       =     int(input('''Enter a single number to be found in the list:\n''').strip())
		__magic_name__       =     linear_search(sequence, target)
		if result != -1:
				print(F"""linear_search({sequence}, {target}) = {result}""")
		else:
				print(F"""{target} was not found in {sequence}""")






