"""simple docstring"""




import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets



@contextmanager
def 				__a       (			):







					"""simple docstring"""





					def filter_log(_lowercase						):
										return False if "This IS expected if you are initializing" in record.msg else True

					lowerCamelCase__		:   str  				=					datasets.utils.logging.get_logger('''transformers.modeling_utils'''						)
					logger.addFilter(_lowercase						)
					try:
										yield
					finally:
										logger.removeFilter(_lowercase						)


UpperCAmelCase       :    Union[str, Any]									=			"\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n"

UpperCAmelCase       :    Optional[int]									=			"\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project's README at https://github.com/Tiiiger/bert_score#readme for more information.\n"

UpperCAmelCase       :    List[str]									=			"\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. 'en').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bertscore = datasets.load_metric(\"bertscore\")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang=\"en\")\n    >>> print([round(v, 2) for v in results[\"f1\"]])\n    [1.0, 1.0]\n"





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







				def   lowercase_		(    self    :int  )      ->					Union[str, Any]:




									"""simple docstring"""



									return datasets.MetricInfo(
									    description=_DESCRIPTION      ,citation=_CITATION      ,homepage='''https://github.com/Tiiiger/bert_score'''      ,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/Tiiiger/bert_score''']      ,reference_urls=[
									        '''https://github.com/Tiiiger/bert_score''',
									        '''https://arxiv.org/abs/1904.09675''',
									    ]      ,)







				def   lowercase_		(    self    :Tuple      ,__UpperCAmelCase    :Tuple      ,__UpperCAmelCase    :Union[str, Any]      ,__UpperCAmelCase    :Union[str, Any]=None      ,__UpperCAmelCase    :str=None      ,__UpperCAmelCase    :Tuple=None      ,__UpperCAmelCase    :Optional[Any]=False      ,__UpperCAmelCase    :Dict=False      ,__UpperCAmelCase    :Optional[Any]=None      ,__UpperCAmelCase    :Any=64      ,__UpperCAmelCase    :str=4      ,__UpperCAmelCase    :Optional[Any]=False      ,__UpperCAmelCase    :List[Any]=False      ,__UpperCAmelCase    :List[Any]=None      ,__UpperCAmelCase    :Optional[int]=False      ,)      ->					Any:




									"""simple docstring"""



									lowerCamelCase__		:   Union[str, Any]  				=					bert_score.utils.get_hash
									lowerCamelCase__		:   Dict  				=					bert_score.BERTScorer

									if version.parse(bert_score.__version__  ) >= version.parse('''0.3.10'''  ):
														lowerCamelCase__		:   List[Any]  				=					functools.partial(__UpperCAmelCase      ,use_fast_tokenizer=__UpperCAmelCase  )
														lowerCamelCase__		:   Tuple  				=					functools.partial(__UpperCAmelCase      ,use_fast_tokenizer=__UpperCAmelCase  )
									elif use_fast_tokenizer:
														raise ImportWarning(
														    '''To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn\'t match this condition.\n'''
														    '''You can install it with `pip install "bert-score>=0.3.10"`.'''  )

									if model_type is None:
														assert lang is not None, "either lang or model_type should be specified"
														lowerCamelCase__		:   List[str]  				=					bert_score.utils.langamodel[lang.lower()]

									if num_layers is None:
														lowerCamelCase__		:   Tuple  				=					bert_score.utils.modelalayers[model_type]

									lowerCamelCase__		:   int  				=					get_hash(
									    model=__UpperCAmelCase      ,num_layers=__UpperCAmelCase      ,idf=__UpperCAmelCase      ,rescale_with_baseline=__UpperCAmelCase      ,use_custom_baseline=baseline_path is not None      ,)

									with filter_logging_context():
														if not hasattr(self      ,'''cached_bertscorer'''  ) or self.cached_bertscorer.hash != hashcode:
																			lowerCamelCase__		:   Tuple  				=					scorer(
																			    model_type=__UpperCAmelCase      ,num_layers=__UpperCAmelCase      ,batch_size=__UpperCAmelCase      ,nthreads=__UpperCAmelCase      ,all_layers=__UpperCAmelCase      ,idf=__UpperCAmelCase      ,device=__UpperCAmelCase      ,lang=__UpperCAmelCase      ,rescale_with_baseline=__UpperCAmelCase      ,baseline_path=__UpperCAmelCase      ,)

									((lowerCamelCase__)				,						(lowerCamelCase__)				,						(lowerCamelCase__))		:   Tuple  				=					self.cached_bertscorer.score(
									    cands=__UpperCAmelCase      ,refs=__UpperCAmelCase      ,verbose=__UpperCAmelCase      ,batch_size=__UpperCAmelCase      ,)
									lowerCamelCase__		:   List[str]  				=					{
									    '''precision''': P.tolist(),
									    '''recall''': R.tolist(),
									    '''f1''': F.tolist(),
									    '''hashcode''': hashcode,
									}
									return output_dict







				def   lowercase_		(    self    :Union[str, Any]      ,__UpperCAmelCase    :List[Any]=None      ,__UpperCAmelCase    :Union[str, Any]=None      ,**__UpperCAmelCase    :Any  )      ->					int:




									"""simple docstring"""



									if references is not None:
														lowerCamelCase__		:   Tuple  				=					[[ref] if isinstance(__UpperCAmelCase      ,__UpperCAmelCase  ) else ref for ref in references]
									super().add_batch(predictions=__UpperCAmelCase      ,references=__UpperCAmelCase      ,**__UpperCAmelCase  )







				def   lowercase_		(    self    :Optional[Any]      ,__UpperCAmelCase    :Any=None      ,__UpperCAmelCase    :str=None      ,**__UpperCAmelCase    :Dict  )      ->					str:




									"""simple docstring"""



									if isinstance(__UpperCAmelCase      ,__UpperCAmelCase  ):
														lowerCamelCase__		:   str  				=					[reference]
									super().add(prediction=__UpperCAmelCase      ,reference=__UpperCAmelCase      ,**__UpperCAmelCase  )







"""simple docstring"""




import math
from collections.abc import Generator



def 				__a       (			_lowercase						):







					"""simple docstring"""





					lowerCamelCase__		:   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							, _lowercase						):
															if (i % j) == 0:
																				break
										else:
															yield i



def 				__a       (			_lowercase						):







					"""simple docstring"""





					lowerCamelCase__		:   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)
										lowerCamelCase__		:   Tuple  				=					int(math.sqrt(_lowercase						)						) + 1
										for j in range(2							, _lowercase						):
															if (i % j) == 0:
																				break
										else:
															yield i



def 				__a       (			_lowercase						):







					"""simple docstring"""





					lowerCamelCase__		:   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):
										lowerCamelCase__		:   Union[str, Any]  				=					int(math.sqrt(_lowercase						)						) + 1
										for j in range(3							, _lowercase							, 2						):
															# As we removed the even numbers, we don't need them now
															if (i % j) == 0:
																				break
										else:
															yield i



def 				__a       (			):







					"""simple docstring"""





					from timeit import timeit

					lowerCamelCase__		:   Any  				=					'''from __main__ import slow_primes, primes, fast_primes'''
					print(timeit('''slow_primes(1_000_000_000_000)'''							, setup=_lowercase							, number=1000000						)						)
					print(timeit('''primes(1_000_000_000_000)'''							, setup=_lowercase							, number=1000000						)						)
					print(timeit('''fast_primes(1_000_000_000_000)'''							, setup=_lowercase							, number=1000000						)						)


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






