





'''simple docstring'''


import datasets

from .evaluate import evaluate


lowerCAmelCase					:     List[str]												='''\
@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}
}
'''

lowerCAmelCase					:     Dict												='''
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.
'''

lowerCAmelCase					:     int												='''
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       a_   (				datasets.Metric							):





     def 							lowercase__      ( self		:					int						):





        """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 							lowercase__      ( self		:					int	,      lowercase		:					Any	,      lowercase		:					Any						):





        """simple docstring"""




        lowercase_			:List[Any]    			= {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
        lowercase_			:str    			= [
            {
                "paragraphs": [
                    {
                        "qas": [
                            {
                                "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
                                "id": ref["id"],
                            }
                            for ref in references
                        ]
                    }
                ]
            }
        ]
        lowercase_			:Optional[int]    			= evaluate(dataset=lowercase	,      predictions=lowercase						)
        return score









'''simple docstring'''


import math
def 							UpperCAmelCase_  (						__lowerCamelCase					:					int ):
   lowercase_			:Dict    			= []
   lowercase_			:List[Any]    			= 2
   lowercase_			:Optional[Any]    			= int(math.sqrt(__lowerCamelCase ) )  # Size of every segment
   lowercase_			:Optional[Any]    			= [True] * (end + 1)
   lowercase_			:Dict    			= []

   while start <= end:
      if temp[start] is True:
         in_prime.append(__lowerCamelCase )
         for i in range(start * start	,end + 1	,__lowerCamelCase ):
            lowercase_			:List[str]    			= False
      start += 1
   prime += in_prime

   lowercase_			:Dict    			= end + 1
   lowercase_			:Dict    			= min(2 * end	,__lowerCamelCase )

   while low <= n:
      lowercase_			:Any    			= [True] * (high - low + 1)
      for each in in_prime:
         lowercase_			:List[Any]    			= math.floor(low / each ) * each
         if t < low:
            t += each

         for j in range(__lowerCamelCase	,high + 1	,__lowerCamelCase ):
            lowercase_			:str    			= False

      for j in range(len(__lowerCamelCase ) ):
         if temp[j] is True:
            prime.append(j + low )

      lowercase_			:Dict    			= high + 1
      lowercase_			:Dict    			= min(high + end	,__lowerCamelCase )

   return prime


print(sieve(10**6))


