




"""simple docstring"""


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       lowercase(  datasets.Metric				):







 '''simple docstring'''





 def 				UpperCamelCase_     (       self:	Dict	):




   '''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 				UpperCamelCase_     (       self:	Union[str, Any],   a_:	Optional[Any],   a_:	int	):




   '''simple docstring'''



   _snake_case							:	int			=						{prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
   _snake_case							:	Tuple			=						[
       {
           """paragraphs""": [
               {
                   """qas""": [
                       {
                           """answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
                           """id""": ref["""id"""],
                       }
                       for ref in references
                   ]
               }
           ]
       }
   ]
   _snake_case							:	int			=						evaluate(dataset=a_,   predictions=a_	)
   return score








"""simple docstring"""


def       UpperCAmelCase__ (snake_case__							:      list[int]	,     snake_case__							:      str					):
  """simple docstring"""

  _snake_case							:	str			=						int(snake_case__					)

  # Initialize Result
  _snake_case							:	str			=						[]

  # Traverse through all denomination
  for denomination in reversed(snake_case__					):
    # Find denominations
    while int(snake_case__					) >= int(snake_case__					):
      total_value -= int(snake_case__					)
      answer.append(snake_case__					)  # Append the "answers" array

  return answer


# Driver Code
if __name__ == "__main__":
     A_      			=       []
     A_      			=       '''0'''

     if (
         input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
         == "y"
     ):
          A_      			=       int(input('''Enter the number of denominations you want to add: ''').strip())

          for i in range(0, n):
               denominations.append(int(input(F'''Denomination {i}: ''').strip()))
          A_      			=       input('''Enter the change you want to make in Indian Currency: ''').strip()
     else:
          # All denominations of Indian Currency if user does not enter
          A_      			=       [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
          A_      			=       input('''Enter the change you want to make: ''').strip()

     if int(value) == 0 or int(value) < 0:
          print('''The total value cannot be zero or negative.''')

     else:
          print(F'''Following is minimal change for {value}: ''')
          A_      			=       find_minimum_change(denominations, value)
          # Print result
          for i in range(len(answer)):
               print(answer[i], end=''' ''')


