"""simple docstring"""






import datasets

from .evaluate import evaluate


lowerCAmelCase__  :  Optional[Any]						     =	'\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n'

lowerCAmelCase__  :  List[str]						     =	'\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n'

lowerCAmelCase__  :  Any						     =	'\nComputes SQuAD scores (F1 and EM).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair as given in the references (see below)\n        - \'prediction_text\': the text of the answer\n    references: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair (see above),\n        - \'answers\': a Dict in the SQuAD dataset format\n            {\n                \'text\': list of possible texts for the answer, as a list of strings\n                \'answer_start\': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_metric = datasets.load_metric("squad")\n    >>> results = squad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 100.0, \'f1\': 100.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,    _KWARGS_DESCRIPTION				)
class 				snake_case       ( datasets.Metric				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	str			):
									return datasets.MetricInfo(
									    description=_DESCRIPTION    ,citation=_CITATION    ,inputs_description=_KWARGS_DESCRIPTION    ,features=datasets.Features(
									        {
									            'predictions': {'id': datasets.Value('string'			), 'prediction_text': datasets.Value('string'			)},
									            'references': {
									                'id': datasets.Value('string'			),
									                'answers': datasets.features.Sequence(
									                    {
									                        'text': datasets.Value('string'			),
									                        'answer_start': datasets.Value('int32'			),
									                    }			),
									            },
									        }			)    ,codebase_urls=['https://rajpurkar.github.io/SQuAD-explorer/']    ,reference_urls=['https://rajpurkar.github.io/SQuAD-explorer/']    ,)







					def 		__lowerCAmelCase						(				self   :	int    ,lowerCamelCase__   :	str    ,lowerCamelCase__   :	str			):
									UpperCAmelCase__             =							{prediction['id']: prediction['prediction_text'] for prediction in predictions}
									UpperCAmelCase__             =							[
									    {
									        'paragraphs': [
									            {
									                'qas': [
									                    {
									                        'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
									                        'id': ref['id'],
									                    }
									                    for ref in references
									                ]
									            }
									        ]
									    }
									]
									UpperCAmelCase__             =							evaluate(dataset=lowerCamelCase__    ,predictions=lowerCamelCase__			)
									return score





import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
__lowerCAmelCase			:     Optional[int]           =   {
    'E': 12.70,
    'T': 9.06,
    'A': 8.17,
    'O': 7.51,
    'I': 6.97,
    'N': 6.75,
    'S': 6.33,
    'H': 6.09,
    'R': 5.99,
    'D': 4.25,
    'L': 4.03,
    'C': 2.78,
    'U': 2.76,
    'M': 2.41,
    'W': 2.36,
    'F': 2.23,
    'G': 2.02,
    'Y': 1.97,
    'P': 1.93,
    'B': 1.29,
    'V': 0.98,
    'K': 0.77,
    'J': 0.15,
    'X': 0.15,
    'Q': 0.10,
    'Z': 0.07,
}
__lowerCAmelCase			:     Optional[Any]           =   'ETAOINSHRDLCUMWFGYPBVKJXQZ'
__lowerCAmelCase			:     Optional[Any]           =   'ABCDEFGHIJKLMNOPQRSTUVWXYZ'

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       {letter: 0 for letter in string.ascii_uppercase}
  for letter in message.upper():
    if letter in LETTERS:
      letter_count[letter] += 1

  return letter_count

def a__    (       A_					):






  '''simple docstring'''





  return x[0]

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       get_letter_count(A_					)
  __magic_name__				     =       {
      freq: [] for letter, freq in letter_to_freq.items()
  }
  for letter in LETTERS:
    freq_to_letter[letter_to_freq[letter]].append(A_					)

  __magic_name__				     =       {}

  for freq in freq_to_letter:
    freq_to_letter[freq].sort(key=ETAOIN.find,     reverse=A_					)
    __magic_name__				     =       """""".join(freq_to_letter[freq]					)

  __magic_name__				     =       list(freq_to_letter_str.items()					)
  freq_pairs.sort(key=A_,     reverse=A_					)

  __magic_name__				     =       [freq_pair[1] for freq_pair in freq_pairs]

  return "".join(A_					)

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       get_frequency_order(A_					)
  __magic_name__				     =       0
  for common_letter in ETAOIN[:6]:
    if common_letter in freq_order[:6]:
      match_score += 1

  for uncommon_letter in ETAOIN[-6:]:
    if uncommon_letter in freq_order[-6:]:
      match_score += 1

  return match_score


if __name__ == "__main__":
    import doctest

    doctest.testmod()

