"""simple docstring"""




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device


if is_torch_available():
					import torch

					from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer

@require_torch
class        __SCREAMING_SNAKE_CASE     (   unittest.TestCase							):







				@slow
				def   lowercase_		(    self    :int  )      ->					Tuple:




									"""simple docstring"""



									lowerCamelCase__		:   Tuple  				=					XLMProphetNetForConditionalGeneration.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased'''  )
									model.to(__UpperCAmelCase  )

									# encoder-decoder outputs
									lowerCamelCase__		:   Tuple  				=					torch.tensor([[17, 9_62_08, 10_34_71, 2]]  ).to(__UpperCAmelCase  )
									lowerCamelCase__		:   Optional[Any]  				=					torch.tensor(
									    [[2, 2_50, 99_53, 34, 6_94_89, 16_20, 32, 11_84_24, 6_24, 2_10, 1_05, 29_13, 10_32, 3_51]]  ).to(__UpperCAmelCase  )
									lowerCamelCase__		:   Optional[int]  				=					model(
									    input_ids=__UpperCAmelCase      ,attention_mask=__UpperCAmelCase      ,encoder_outputs=__UpperCAmelCase      ,decoder_input_ids=__UpperCAmelCase  )
									lowerCamelCase__		:   Tuple  				=					output[0]
									lowerCamelCase__		:   Optional[int]  				=					torch.Size((1, 14, 25_00_12)  )
									self.assertEqual(output_predited_logis.shape      ,__UpperCAmelCase  )
									lowerCamelCase__		:   Optional[int]  				=					torch.tensor(
									    [[[-6.3_986, -8.2_391, 12.5_189], [-6.3_289, -8.0_864, 12.6_211], [-6.2_418, -8.0_445, 12.7_968]]]  ).to(__UpperCAmelCase  )
									self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]      ,__UpperCAmelCase      ,atol=1E-4  )  )

									# encoder outputs
									lowerCamelCase__		:   Optional[Any]  				=					model.prophetnet.encoder(__UpperCAmelCase  )[0]
									lowerCamelCase__		:   Any  				=					torch.tensor(
									    [[[-1.4_260, -0.7_628, 0.8_453], [-1.4_719, -0.1_391, 0.7_807], [-1.7_678, 0.0_114, 0.4_646]]]  ).to(__UpperCAmelCase  )
									lowerCamelCase__		:   List[str]  				=					torch.Size((1, 4, 10_24)  )
									self.assertEqual(encoder_outputs.shape      ,__UpperCAmelCase  )
									self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]      ,__UpperCAmelCase      ,atol=1E-4  )  )

									# decoder outputs
									lowerCamelCase__		:   Tuple  				=					model.prophetnet.decoder(
									    __UpperCAmelCase      ,encoder_hidden_states=__UpperCAmelCase      ,)
									lowerCamelCase__		:   str  				=					decoder_outputs[1].view(1      ,model.config.ngram      ,14      ,-1  )
									lowerCamelCase__		:   str  				=					model.lm_head(__UpperCAmelCase  )
									lowerCamelCase__		:   List[str]  				=					predicting_streams_logits[:, 0]
									self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]      ,__UpperCAmelCase      ,atol=1E-4  )  )







				@slow
				def   lowercase_		(    self    :Optional[int]  )      ->					int:




									"""simple docstring"""



									lowerCamelCase__		:   Dict  				=					XLMProphetNetForConditionalGeneration.from_pretrained(
									    '''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''  )
									model.to(__UpperCAmelCase  )

									lowerCamelCase__		:   List[Any]  				=					torch.tensor([[17, 9_62_08, 10_34_71, 2]]  ).to(__UpperCAmelCase  )
									lowerCamelCase__		:   Optional[Any]  				=					torch.tensor(
									    [[2, 2_50, 99_53, 34, 6_94_89, 16_20, 32, 11_84_24, 6_24, 2_10, 1_05, 29_13, 10_32, 3_51]]  ).to(__UpperCAmelCase  )
									lowerCamelCase__		:   Optional[Any]  				=					model(
									    input_ids=__UpperCAmelCase      ,attention_mask=__UpperCAmelCase      ,encoder_outputs=__UpperCAmelCase      ,decoder_input_ids=__UpperCAmelCase  )
									lowerCamelCase__		:   Dict  				=					output[0]
									lowerCamelCase__		:   Any  				=					torch.Size((1, 14, 25_00_12)  )
									self.assertEqual(output_predited_logis.shape      ,__UpperCAmelCase  )
									# compare the actual values for a slice.
									lowerCamelCase__		:   List[str]  				=					torch.tensor(
									    [[[-9.2_253, -9.7_173, -6.3_529], [-7.6_701, -9.0_145, -1.9_382], [-8.0_195, -7.0_004, -0.1_523]]]  ).to(__UpperCAmelCase  )

									self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]      ,__UpperCAmelCase      ,atol=1E-4  )  )







				@slow
				def   lowercase_		(    self    :Optional[Any]  )      ->					Optional[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   str  				=					XLMProphetNetForConditionalGeneration.from_pretrained(
									    '''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''  )
									model.to(__UpperCAmelCase  )
									lowerCamelCase__		:   Any  				=					5_12

									lowerCamelCase__		:   Dict  				=					XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''  )

									lowerCamelCase__		:   Optional[int]  				=					(
									    '''Microsoft Corporation intends to officially end free support for the Windows 7 operating system after'''
									    ''' January 14, 2020, according to the official portal of the organization. From that day, users of this'''
									    ''' system will not be able to receive security updates, which could make their computers vulnerable to'''
									    ''' cyber attacks.'''
									)
									lowerCamelCase__		:   List[Any]  				=					(
									    '''орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7'''
									    ''' после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи'''
									    ''' этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми'''
									    ''' к кибератакам.'''
									)
									lowerCamelCase__		:   Optional[Any]  				=					(
									    '''根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。'''
									)

									lowerCamelCase__		:   Any  				=					tokenizer(
									    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]      ,padding=__UpperCAmelCase      ,max_length=2_55      ,return_tensors='''pt'''  ).input_ids
									lowerCamelCase__		:   Optional[int]  				=					input_ids.to(__UpperCAmelCase  )

									lowerCamelCase__		:   Dict  				=					model.generate(
									    __UpperCAmelCase      ,num_beams=10      ,length_penalty=1.0      ,no_repeat_ngram_size=3      ,early_stopping=__UpperCAmelCase  )
									lowerCamelCase__		:   Optional[int]  				=					[tokenizer.decode(__UpperCAmelCase      ,skip_special_tokens=__UpperCAmelCase  ) for g in summary_ids]
									lowerCamelCase__		:   Union[str, Any]  				=					'''Microsoft to end Windows 7 free support after January 14, 2020'''
									lowerCamelCase__		:   str  				=					'''Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года'''
									lowerCamelCase__		:   List[Any]  				=					'''微软打算终止对Windows 7操作系统的免费支持'''
									self.assertListEqual(
									    [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH]      ,__UpperCAmelCase      ,)

									lowerCamelCase__		:   Tuple  				=					model.generate(
									    __UpperCAmelCase      ,num_beams=1      ,length_penalty=1.0      ,no_repeat_ngram_size=3      ,early_stopping=__UpperCAmelCase  )
									lowerCamelCase__		:   Tuple  				=					[
									    tokenizer.convert_ids_to_tokens(__UpperCAmelCase      ,skip_special_tokens=__UpperCAmelCase  ) for g in summary_ids_beama
									]
									lowerCamelCase__		:   str  				=					'''▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7'''.split(''' '''  )
									lowerCamelCase__		:   str  				=					'''▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года'''.split(
									    ''' '''  )
									lowerCamelCase__		:   List[Any]  				=					'''微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持'''.split(''' '''  )
									self.assertListEqual(
									    [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK]      ,__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()






