




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		bool:

    """simple docstring"""


    if not isinstance(lowercase_   ,       lowercase_ ):
        raise ValueError('''Input series is not valid, valid series - [2, 4, 8]''' )
    if len(lowercase_ ) == 0:
        raise ValueError('''Input list must be a non empty list''' )
    if len(lowercase_ ) == 1:
        return True
    try:
        __UpperCamelCase											=  series[1] / series[0]
        for index in range(len(lowercase_ ) - 1 ):
            if series[index + 1] / series[index] != common_ratio:
                return False
    except ZeroDivisionError:
        return False
    return True




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		float:

    """simple docstring"""


    if not isinstance(lowercase_   ,       lowercase_ ):
        raise ValueError('''Input series is not valid, valid series - [2, 4, 8]''' )
    if len(lowercase_ ) == 0:
        raise ValueError('''Input list must be a non empty list''' )
    __UpperCamelCase											=  1
    for value in series:
        answer *= value
    return pow(lowercase_   ,       1 / len(lowercase_ ) )


if __name__ == "__main__":
 import doctest

 doctest.testmod()












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




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		Dict:

    """simple docstring"""


    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_ ):
        __UpperCamelCase											=  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    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		List[str]:

    """simple docstring"""


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




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		Any:

    """simple docstring"""


    return normalize_answer(lowercase_ ) == normalize_answer(lowercase_ )




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		int:

    """simple docstring"""


    __UpperCamelCase											=  []
    for ground_truth in ground_truths:
        __UpperCamelCase											=  metric_fn(lowercase_   ,       lowercase_ )
        scores_for_ground_truths.append(lowercase_ )
    return max(lowercase_ )




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		Optional[int]:

    """simple docstring"""


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

            __UpperCamelCase											=  [x['''text'''] for x in qa['''answers''']]
            __UpperCamelCase											=  predictions[qa['''id''']]

            __UpperCamelCase											=  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_ )

    __UpperCamelCase											=  exact_match / total
    __UpperCamelCase											=  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)






