
'''simple docstring'''







import datasets

from .evaluate import evaluate


lowercase    :							List[str]      =					"\\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"

lowercase    :							List[Any]      =					"\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"

lowercase    :							Tuple      =					"\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 						__UpperCAmelCase      (					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    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






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








def 	UpperCamelCase__(       UpperCamelCase__			:       Dict  )->Dict:
       # if the collection is empty, returns empty
       if collection == []:
              return []

       # get some information about the collection
       A__											=						len(UpperCamelCase__  )
       A__											=						max(UpperCamelCase__  )
       A__											=						min(UpperCamelCase__  )

       # create the counting array
       A__											=						coll_max + 1 - coll_min
       A__											=						[0] * counting_arr_length

       # count how much a number appears in the collection
       for number in collection:
              counting_arr[number - coll_min] += 1

       # sum each position with it's predecessors. now, counting_arr[i] tells
       # us how many elements <= i has in the collection
       for i in range(1 ,     UpperCamelCase__  ):
              A__											=						counting_arr[i] + counting_arr[i - 1]

       # create the output collection
       A__											=						[0] * coll_len

       # place the elements in the output, respecting the original order (stable
       # sort) from end to begin, updating counting_arr
       for i in reversed(range(0 ,     UpperCamelCase__  )  ):
              A__											=						collection[i]
              counting_arr[collection[i] - coll_min] -= 1

       return ordered


def 	UpperCamelCase__(       UpperCamelCase__			:       Optional[int]  )->Tuple:
       return "".join([chr(UpperCamelCase__  ) for i in counting_sort([ord(UpperCamelCase__  ) for c in string]  )]  )


if __name__ == "__main__":
       # Test string sort
       assert counting_sort_string('thisisthestring') == "eghhiiinrsssttt"

       a__:      Dict									=		input('Enter numbers separated by a comma:\n').strip()
       a__:      Any									=		[int(item) for item in user_input.split(',')]
       print(counting_sort(unsorted))





