



import datasets

from .evaluate import evaluate


a_								=       """\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
"""

a_								=       """
This metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.
"""

a_								=       """
Computes SQuAD scores (F1 and EM).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair as given in the references (see below)
        - 'prediction_text': the text of the answer
    references: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair (see above),
        - 'answers': a Dict in the SQuAD dataset format
            {
                'text': list of possible texts for the answer, as a list of strings
                'answer_start': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    'exact_match': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
Examples:

    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]
    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]
    >>> squad_metric = datasets.load_metric(\"squad\")
    >>> results = squad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 100.0, 'f1': 100.0}
"""



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,						_KWARGS_DESCRIPTION )
class 			__lowerCAmelCase  (						datasets.Metric ):




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    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						,					__UpperCAmelCase						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     {prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
    __lowerCamelCase    				=     [
        {
            '''paragraphs''': [
                {
                    '''qas''': [
                        {
                            '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
                            '''id''': ref['''id'''],
                        }
                        for ref in references
                    ]
                }
            ]
        }
    ]
    __lowerCamelCase    				=     evaluate(dataset=__UpperCAmelCase						,					predictions=__UpperCAmelCase					)
    return score











def 		a__      (		_UpperCamelCase      :			list[int]				):
   if not numbers:
      return 0

   if not isinstance(_UpperCamelCase			,(list, tuple)				) or not all(
       isinstance(_UpperCamelCase			,_UpperCamelCase				) for number in numbers				):
      raise ValueError('''numbers must be an iterable of integers'''				)

   __lowerCamelCase    				=     __lowerCamelCase    				=     __lowerCamelCase    				=     numbers[0]

   for i in range(1			,len(_UpperCamelCase				)				):
      # update the maximum and minimum subarray products
      __lowerCamelCase    				=     numbers[i]
      if number < 0:
         __lowerCamelCase				,__lowerCamelCase    				=     min_till_now, max_till_now
      __lowerCamelCase    				=     max(_UpperCamelCase			,max_till_now * number				)
      __lowerCamelCase    				=     min(_UpperCamelCase			,min_till_now * number				)

      # update the maximum product found till now
      __lowerCamelCase    				=     max(_UpperCamelCase			,_UpperCamelCase				)

   return max_prod






