





'''simple docstring'''




import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


__SCREAMING_SNAKE_CASE     :int       =				'''\
@inproceedings{lin-2004-rouge,
    title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",
    author = "Lin, Chin-Yew",
    booktitle = "Text Summarization Branches Out",
    month = jul,
    year = "2004",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W04-1013",
    pages = "74--81",
}
'''

__SCREAMING_SNAKE_CASE     :Dict       =				'''\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
'''

__SCREAMING_SNAKE_CASE     :Union[str, Any]       =				'''
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,
        `"rougeL"`: Longest common subsequence based scoring.
        `"rougeLSum"`: rougeLsum splits text using `"\n"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric(\'rouge\')
    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']
    >>> print(results["rouge1"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results["rouge1"].mid.fmeasure)
    1.0
'''







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




 def      lowercase	(					self       :    Dict    ):
      return datasets.MetricInfo(
          description=_DESCRIPTION	,			citation=_CITATION	,			inputs_description=_KWARGS_DESCRIPTION	,			features=datasets.Features(
              {
                  "predictions": datasets.Value("string"	,			id="sequence"    ),
                  "references": datasets.Value("string"	,			id="sequence"    ),
              }    )	,			codebase_urls=["https://github.com/google-research/google-research/tree/master/rouge"]	,			reference_urls=[
              "https://en.wikipedia.org/wiki/ROUGE_(metric)",
              "https://github.com/google-research/google-research/tree/master/rouge",
          ]	,			)




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    Optional[int]	,			snake_case_       :    List[str]	,			snake_case_       :    List[Any]=None	,			snake_case_       :    Tuple=True	,			snake_case_       :    List[str]=False    ):
      if rouge_types is None:
           _UpperCAmelCase     							=   ["""rouge1""", """rouge2""", """rougeL""", """rougeLsum"""]

      _UpperCAmelCase     							=   rouge_scorer.RougeScorer(rouge_types=snake_case_	,			use_stemmer=snake_case_    )
      if use_aggregator:
           _UpperCAmelCase     							=   scoring.BootstrapAggregator()
      else:
           _UpperCAmelCase     							=   []

      for ref, pred in zip(snake_case_	,			snake_case_    ):
           _UpperCAmelCase     							=   scorer.score(snake_case_	,			snake_case_    )
           if use_aggregator:
                aggregator.add_scores(snake_case_    )
           else:
                scores.append(snake_case_    )

      if use_aggregator:
           _UpperCAmelCase     							=   aggregator.aggregate()
      else:
           _UpperCAmelCase     							=   {}
           for key in scores[0]:
                _UpperCAmelCase     							=   [score[key] for score in scores]

      return result









'''simple docstring'''




from __future__ import annotations

import math



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			if num <= 0:
						A_	:						List[Any]	=     f'{num}: Invalid input, please enter a positive integer.'
						raise ValueError(lowerCamelCase__    )

			A_	:						Dict	=     [True] * (num + 1)
			A_	:						List[Any]	=     []
			A_	:						Tuple	=     2
			A_	:						Optional[int]	=     int(math.sqrt(lowerCamelCase__    )    )

			while start <= end:
						# If start is a prime
						if sieve[start] is True:
									prime.append(lowerCamelCase__    )

									# Set multiples of start be False
									for i in range(start * start						,      num + 1						,      lowerCamelCase__    ):
												if sieve[i] is True:
															A_	:						List[Any]	=     False

						start += 1

			for j in range(end + 1						,      num + 1    ):
						if sieve[j] is True:
									prime.append(lowerCamelCase__    )

			return prime


if __name__ == "__main__":
						print(prime_sieve(int(input('''Enter a positive integer: ''').strip())))