



"""simple docstring"""

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
			import torch

			from transformers import XLMRobertaModel







@require_sentencepiece
@require_tokenizers
@require_torch
class    __lowercase     (					unittest.TestCase):







						"""simple docstring"""







						@slow
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		List[Any] 					=					XLMRobertaModel.from_pretrained("""xlm-roberta-base"""       )
											snake_case_					:		Dict 					=					torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]       )
											# The dog is cute and lives in the garden house

											snake_case_					:		str 					=					torch.Size((1, 12, 7_68)       )  # batch_size, sequence_length, embedding_vector_dim
											snake_case_					:		Any 					=					torch.tensor(
											    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]       )
											#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
											#  xlmr.eval()
											#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
											with torch.no_grad():
																snake_case_					:		Union[str, Any] 					=					model(lowercase__       )["""last_hidden_state"""].detach()
											self.assertEqual(output.shape   ,		lowercase__       )
											# compare the actual values for a slice of last dim
											self.assertTrue(torch.allclose(output[:, :, -1]   ,		lowercase__   ,		atol=1e-3       )       )







						@slow
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		List[str] 					=					XLMRobertaModel.from_pretrained("""xlm-roberta-large"""       )
											snake_case_					:		Tuple 					=					torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]       )
											# The dog is cute and lives in the garden house

											snake_case_					:		List[Any] 					=					torch.Size((1, 12, 10_24)       )  # batch_size, sequence_length, embedding_vector_dim
											snake_case_					:		List[str] 					=					torch.tensor(
											    [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]       )
											#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
											#  xlmr.eval()
											#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
											with torch.no_grad():
																snake_case_					:		Optional[int] 					=					model(lowercase__       )["""last_hidden_state"""].detach()
											self.assertEqual(output.shape   ,		lowercase__       )
											# compare the actual values for a slice of last dim
											self.assertTrue(torch.allclose(output[:, :, -1]   ,		lowercase__   ,		atol=1e-3       )       )









"""simple docstring"""

import random







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





					snake_case_					:		Union[str, Any] 					=					num - 1
					snake_case_					:		List[str] 					=					0

					while s % 2 == 0:
										snake_case_					:		str 					=					s // 2
										t += 1

					for _ in range(5			):
										snake_case_					:		List[Any] 					=					random.randrange(2				,    num - 1			)
										snake_case_					:		Dict 					=					pow(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										if v != 1:
															snake_case_					:		int 					=					0
															while v != (num - 1):
																				if i == t - 1:
																									return False
																				else:
																									snake_case_					:		str 					=					i + 1
																									snake_case_					:		int 					=					(v**2) % num
					return True







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





					if num < 2:
										return False

					snake_case_					:		Dict 					=					[
					    2,
					    3,
					    5,
					    7,
					    1_1,
					    1_3,
					    1_7,
					    1_9,
					    2_3,
					    2_9,
					    3_1,
					    3_7,
					    4_1,
					    4_3,
					    4_7,
					    5_3,
					    5_9,
					    6_1,
					    6_7,
					    7_1,
					    7_3,
					    7_9,
					    8_3,
					    8_9,
					    9_7,
					    1_0_1,
					    1_0_3,
					    1_0_7,
					    1_0_9,
					    1_1_3,
					    1_2_7,
					    1_3_1,
					    1_3_7,
					    1_3_9,
					    1_4_9,
					    1_5_1,
					    1_5_7,
					    1_6_3,
					    1_6_7,
					    1_7_3,
					    1_7_9,
					    1_8_1,
					    1_9_1,
					    1_9_3,
					    1_9_7,
					    1_9_9,
					    2_1_1,
					    2_2_3,
					    2_2_7,
					    2_2_9,
					    2_3_3,
					    2_3_9,
					    2_4_1,
					    2_5_1,
					    2_5_7,
					    2_6_3,
					    2_6_9,
					    2_7_1,
					    2_7_7,
					    2_8_1,
					    2_8_3,
					    2_9_3,
					    3_0_7,
					    3_1_1,
					    3_1_3,
					    3_1_7,
					    3_3_1,
					    3_3_7,
					    3_4_7,
					    3_4_9,
					    3_5_3,
					    3_5_9,
					    3_6_7,
					    3_7_3,
					    3_7_9,
					    3_8_3,
					    3_8_9,
					    3_9_7,
					    4_0_1,
					    4_0_9,
					    4_1_9,
					    4_2_1,
					    4_3_1,
					    4_3_3,
					    4_3_9,
					    4_4_3,
					    4_4_9,
					    4_5_7,
					    4_6_1,
					    4_6_3,
					    4_6_7,
					    4_7_9,
					    4_8_7,
					    4_9_1,
					    4_9_9,
					    5_0_3,
					    5_0_9,
					    5_2_1,
					    5_2_3,
					    5_4_1,
					    5_4_7,
					    5_5_7,
					    5_6_3,
					    5_6_9,
					    5_7_1,
					    5_7_7,
					    5_8_7,
					    5_9_3,
					    5_9_9,
					    6_0_1,
					    6_0_7,
					    6_1_3,
					    6_1_7,
					    6_1_9,
					    6_3_1,
					    6_4_1,
					    6_4_3,
					    6_4_7,
					    6_5_3,
					    6_5_9,
					    6_6_1,
					    6_7_3,
					    6_7_7,
					    6_8_3,
					    6_9_1,
					    7_0_1,
					    7_0_9,
					    7_1_9,
					    7_2_7,
					    7_3_3,
					    7_3_9,
					    7_4_3,
					    7_5_1,
					    7_5_7,
					    7_6_1,
					    7_6_9,
					    7_7_3,
					    7_8_7,
					    7_9_7,
					    8_0_9,
					    8_1_1,
					    8_2_1,
					    8_2_3,
					    8_2_7,
					    8_2_9,
					    8_3_9,
					    8_5_3,
					    8_5_7,
					    8_5_9,
					    8_6_3,
					    8_7_7,
					    8_8_1,
					    8_8_3,
					    8_8_7,
					    9_0_7,
					    9_1_1,
					    9_1_9,
					    9_2_9,
					    9_3_7,
					    9_4_1,
					    9_4_7,
					    9_5_3,
					    9_6_7,
					    9_7_1,
					    9_7_7,
					    9_8_3,
					    9_9_1,
					    9_9_7,
					]

					if num in low_primes:
										return True

					for prime in low_primes:
										if (num % prime) == 0:
															return False

					return rabin_miller(SCREAMING_SNAKE_CASE__			)







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int = 1_0_2_4			):



					"""simple docstring"""





					while True:
										snake_case_					:		Tuple 					=					random.randrange(2 ** (keysize - 1)				,    2 ** (keysize)			)
										if is_prime_low_num(SCREAMING_SNAKE_CASE__			):
															return num


if __name__ == "__main__":
			a_			   =					generate_large_prime()
			print(('''Prime number:''', num))
			print(('''is_prime_low_num:''', is_prime_low_num(num)))




