





"""simple docstring"""
import sacrebleu as scb
from packaging import version

import datasets


lowerCamelCase_			    =	'\\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

lowerCamelCase_			    =	'\\nSacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.\nInspired by Rico Sennrich\'s `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.\nIt also knows all the standard test sets and handles downloading, processing, and tokenization for you.\n\nSee the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.\n'

lowerCamelCase_			    =	'\nProduces BLEU scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.\n    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).\n    smooth_method (`str`): The smoothing method to use, defaults to `\'exp\'`. Possible values are:\n        - `\'none\'`: no smoothing\n        - `\'floor\'`: increment zero counts\n        - `\'add-k\'`: increment num/denom by k for n>1\n        - `\'exp\'`: exponential decay\n    smooth_value (`float`): The smoothing value. Only valid when `smooth_method=\'floor\'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method=\'add-k\'` (in which case `smooth_value` defaults to `1`).\n    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `\'zh\'` for Chinese, `\'ja-mecab\'` for Japanese and `\'13a\'` (mteval) otherwise. Possible values are:\n        - `\'none\'`: No tokenization.\n        - `\'zh\'`: Chinese tokenization.\n        - `\'13a\'`: mimics the `mteval-v13a` script from Moses.\n        - `\'intl\'`: International tokenization, mimics the `mteval-v14` script from Moses\n        - `\'char\'`: Language-agnostic character-level tokenization.\n        - `\'ja-mecab\'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).\n    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.\n    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.\n    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.\n\nReturns:\n    \'score\': BLEU score,\n    \'counts\': Counts,\n    \'totals\': Totals,\n    \'precisions\': Precisions,\n    \'bp\': Brevity penalty,\n    \'sys_len\': predictions length,\n    \'ref_len\': reference length,\n\nExamples:\n\n    Example 1:\n        >>> predictions = ["hello there general kenobi", "foo bar foobar"]\n        >>> references = [["hello there general kenobi", "hello there !"], ["foo bar foobar", "foo bar foobar"]]\n        >>> sacrebleu = datasets.load_metric("sacrebleu")\n        >>> results = sacrebleu.compute(predictions=predictions, references=references)\n        >>> print(list(results.keys()))\n        [\'score\', \'counts\', \'totals\', \'precisions\', \'bp\', \'sys_len\', \'ref_len\']\n        >>> print(round(results["score"], 1))\n        100.0\n\n    Example 2:\n        >>> predictions = ["hello there general kenobi",\n        ...                 "on our way to ankh morpork"]\n        >>> references = [["hello there general kenobi", "hello there !"],\n        ...                 ["goodbye ankh morpork", "ankh morpork"]]\n        >>> sacrebleu = datasets.load_metric("sacrebleu")\n        >>> results = sacrebleu.compute(predictions=predictions,\n        ...                             references=references)\n        >>> print(list(results.keys()))\n        [\'score\', \'counts\', \'totals\', \'precisions\', \'bp\', \'sys_len\', \'ref_len\']\n        >>> print(round(results["score"], 1))\n        39.8\n'






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




       def 					_UpperCamelCase  (		self							)    ->   List[str]:





             """simple docstring"""






             if version.parse(scb.__version__							) < version.parse('''1.4.12'''							):
                   raise ImportWarning(
                       '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
                       '''You can install it with `pip install "sacrebleu>=1.4.12"`.'''							)
             return datasets.MetricInfo(
                 description=_DESCRIPTION	,citation=_CITATION	,homepage='''https://github.com/mjpost/sacreBLEU'''	,inputs_description=_KWARGS_DESCRIPTION	,features=datasets.Features(
                     {
                         '''predictions''': datasets.Value('''string'''	,id='''sequence'''							),
                         '''references''': datasets.Sequence(datasets.Value('''string'''	,id='''sequence'''							)	,id='''references'''							),
                     }							)	,codebase_urls=['''https://github.com/mjpost/sacreBLEU''']	,reference_urls=[
                     '''https://github.com/mjpost/sacreBLEU''',
                     '''https://en.wikipedia.org/wiki/BLEU''',
                     '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
                 ]	,)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__="exp"	,SCREAMING_SNAKE_CASE__=None	,SCREAMING_SNAKE_CASE__=False	,SCREAMING_SNAKE_CASE__=False	,SCREAMING_SNAKE_CASE__=None	,SCREAMING_SNAKE_CASE__=False	,)    ->   List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :List[str]	       =						len(references[0]							)
             if any(len(SCREAMING_SNAKE_CASE__							) != references_per_prediction for refs in references							):
                   raise ValueError('''Sacrebleu requires the same number of references for each prediction'''							)
             __SCREAMING_SNAKE_CASE     :Tuple	       =						[[refs[i] for refs in references] for i in range(SCREAMING_SNAKE_CASE__							)]
             __SCREAMING_SNAKE_CASE     :Dict	       =						scb.corpus_bleu(
                 SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,smooth_method=SCREAMING_SNAKE_CASE__	,smooth_value=SCREAMING_SNAKE_CASE__	,force=SCREAMING_SNAKE_CASE__	,lowercase=SCREAMING_SNAKE_CASE__	,use_effective_order=SCREAMING_SNAKE_CASE__	,**({'''tokenize''': tokenize} if tokenize else {})	,)
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						{
                 '''score''': output.score,
                 '''counts''': output.counts,
                 '''totals''': output.totals,
                 '''precisions''': output.precisions,
                 '''bp''': output.bp,
                 '''sys_len''': output.sys_len,
                 '''ref_len''': output.ref_len,
             }
             return output_dict






"""simple docstring"""
import math
from collections.abc import Generator



def   __lowerCamelCase     (							a_  :	int							)    ->   Generator[int, None, None]:
      __SCREAMING_SNAKE_CASE     :Generator	       =						(i for i in range(1				,     (max_n + 1)							))
      for i in (n for n in numbers if n > 1):
            for j in range(2				,     a_							):
                  if (i % j) == 0:
                        break
            else:
                  yield i



def   __lowerCamelCase     (							a_  :	int							)    ->   Generator[int, None, None]:
      __SCREAMING_SNAKE_CASE     :Generator	       =						(i for i in range(1				,     (max_n + 1)							))
      for i in (n for n in numbers if n > 1):
            # only need to check for factors up to sqrt(i)
            __SCREAMING_SNAKE_CASE     :Tuple	       =						int(math.sqrt(a_							)							) + 1
            for j in range(2				,     a_							):
                  if (i % j) == 0:
                        break
            else:
                  yield i



def   __lowerCamelCase     (							a_  :	int							)    ->   Generator[int, None, None]:
      __SCREAMING_SNAKE_CASE     :Generator	       =						(i for i in range(1				,     (max_n + 1)				,     2							))
      # It's useless to test even numbers as they will not be prime
      if max_n > 2:
            yield 2  # Because 2 will not be tested, it's necessary to yield it now
      for i in (n for n in numbers if n > 1):
            __SCREAMING_SNAKE_CASE     :int	       =						int(math.sqrt(a_							)							) + 1
            for j in range(3				,     a_				,     2							):
                  # As we removed the even numbers, we don't need them now
                  if (i % j) == 0:
                        break
            else:
                  yield i



def   __lowerCamelCase     (							)    ->   Union[str, Any]:
      from timeit import timeit

      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						'''from __main__ import slow_primes, primes, fast_primes'''
      print(timeit('''slow_primes(1_000_000_000_000)'''				,     setup=a_				,     number=1_00_00_00							)							)
      print(timeit('''primes(1_000_000_000_000)'''				,     setup=a_				,     number=1_00_00_00							)							)
      print(timeit('''fast_primes(1_000_000_000_000)'''				,     setup=a_				,     number=1_00_00_00							)							)


if __name__ == "__main__":
  lowerCamelCase_          =	int(input("Calculate primes up to:\n>> ").strip())
  for ret in primes(number):
    print(ret)
  benchmark()