





'''simple docstring'''

import datasets

from .evaluate import evaluate


a   	=    "\\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"

a   	=    "\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"

a   	=    "\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 __a			( datasets.Metric      ):





   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''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					:      List[str]					,lowerCamelCase					:      str					,lowerCamelCase					:      Dict     ):

      '''simple docstring'''

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







def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int = 2_00_00_00       )  ->     int:
						_lowercase					       =					[0 for i in range(n + 1       )]
						_lowercase					       =					1
						_lowercase					       =					1

						for i in range(2		,   int(n**0.5       ) + 1       ):
												if primality_list[i] == 0:
																		for j in range(i * i		,   n + 1		,   SCREAMING_SNAKE_CASE_       ):
																								_lowercase					       =					1
						_lowercase					       =					0
						for i in range(SCREAMING_SNAKE_CASE_       ):
												if primality_list[i] == 0:
																		sum_of_primes += i
						return sum_of_primes


if __name__ == "__main__":
		print(F'{solution() = }')