
import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets


@contextmanager
def  A			(   )  ->    Optional[Any]:
		'''simple docstring'''

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

		UpperCamelCase            =	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 							lowercase   (						datasets.Metric  ):



		def __UpperCamelCase					(					self			)    ->   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 __UpperCamelCase					(					self							, A_							, A_							, A_=None							, A_=None							, A_=None							, A_=False							, A_=False							, A_=None							, A_=64							, A_=4							, A_=False							, A_=False							, A_=None							, A_=False							, )    ->   Any:
				"""simple docstring"""
				UpperCamelCase            =	bert_score.utils.get_hash
				UpperCamelCase            =	bert_score.BERTScorer

				if version.parse(bert_score.__version__			) >= version.parse('0.3.10'			):
						UpperCamelCase            =	functools.partial(__UpperCAmelCase							, use_fast_tokenizer=__UpperCAmelCase			)
						UpperCamelCase            =	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"
						UpperCamelCase            =	bert_score.utils.langamodel[lang.lower()]

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

				UpperCamelCase            =	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:
								UpperCamelCase            =	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							, )

				(UpperCamelCase)            =	self.cached_bertscorer.score(
				    cands=__UpperCAmelCase							, refs=__UpperCAmelCase							, verbose=__UpperCAmelCase							, batch_size=__UpperCAmelCase							, )
				UpperCamelCase            =	{
				    '''precision''': P.tolist(),
				    '''recall''': R.tolist(),
				    '''f1''': F.tolist(),
				    '''hashcode''': hashcode,
				}
				return output_dict



		def __UpperCamelCase					(					self							, A_=None							, A_=None							, **A_			)    ->   int:
				"""simple docstring"""



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



		def __UpperCamelCase					(					self							, A_=None							, A_=None							, **A_			)    ->   str:
				"""simple docstring"""



				if isinstance(__UpperCAmelCase							, __UpperCAmelCase			):
						UpperCamelCase            =	[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()






