



"""simple docstring"""

import unittest

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    MPNetForMaskedLM,
			    MPNetForMultipleChoice,
			    MPNetForQuestionAnswering,
			    MPNetForSequenceClassification,
			    MPNetForTokenClassification,
			    MPNetModel,
			)







class    __lowercase     :







						"""simple docstring"""







						def __init__(self   ,		lowercase__   ,		lowercase__=13   ,		lowercase__=7   ,		lowercase__=True   ,		lowercase__=True   ,		lowercase__=False   ,		lowercase__=True   ,		lowercase__=99   ,		lowercase__=64   ,		lowercase__=5   ,		lowercase__=4   ,		lowercase__=64   ,		lowercase__="gelu"   ,		lowercase__=0.1   ,		lowercase__=0.1   ,		lowercase__=5_12   ,		lowercase__=16   ,		lowercase__=2   ,		lowercase__=0.02   ,		lowercase__=3   ,		lowercase__=4   ,		lowercase__=None   ,		):
											snake_case_					:		List[Any] 					=					parent
											snake_case_					:		Any 					=					batch_size
											snake_case_					:		Tuple 					=					seq_length
											snake_case_					:		Any 					=					is_training
											snake_case_					:		List[str] 					=					use_input_mask
											snake_case_					:		Optional[int] 					=					use_token_type_ids
											snake_case_					:		Optional[int] 					=					use_labels
											snake_case_					:		Optional[int] 					=					vocab_size
											snake_case_					:		List[str] 					=					hidden_size
											snake_case_					:		List[str] 					=					num_hidden_layers
											snake_case_					:		Union[str, Any] 					=					num_attention_heads
											snake_case_					:		Union[str, Any] 					=					intermediate_size
											snake_case_					:		Tuple 					=					hidden_act
											snake_case_					:		Dict 					=					hidden_dropout_prob
											snake_case_					:		Optional[Any] 					=					attention_probs_dropout_prob
											snake_case_					:		str 					=					max_position_embeddings
											snake_case_					:		Optional[Any] 					=					type_vocab_size
											snake_case_					:		Dict 					=					type_sequence_label_size
											snake_case_					:		str 					=					initializer_range
											snake_case_					:		Optional[int] 					=					num_labels
											snake_case_					:		str 					=					num_choices
											snake_case_					:		int 					=					scope







						def 						__UpperCamelCase							(self       ):
											return MPNetConfig.from_pretrained("""microsoft/mpnet-base"""       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		int 					=					ids_tensor([self.batch_size, self.seq_length]   ,		self.vocab_size       )

											snake_case_					:		Optional[Any] 					=					None
											if self.use_input_mask:
																snake_case_					:		str 					=					random_attention_mask([self.batch_size, self.seq_length]       )

											snake_case_					:		Dict 					=					None
											snake_case_					:		Optional[int] 					=					None
											snake_case_					:		List[Any] 					=					None
											if self.use_labels:
																snake_case_					:		str 					=					ids_tensor([self.batch_size]   ,		self.type_sequence_label_size       )
																snake_case_					:		Union[str, Any] 					=					ids_tensor([self.batch_size, self.seq_length]   ,		self.num_labels       )
																snake_case_					:		Optional[int] 					=					ids_tensor([self.batch_size]   ,		self.num_choices       )

											snake_case_					:		str 					=					self.get_config()
											return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels







						def 						__UpperCamelCase							(self       ):
											return MPNetConfig(
											    vocab_size=self.vocab_size   ,		hidden_size=self.hidden_size   ,		num_hidden_layers=self.num_hidden_layers   ,		num_attention_heads=self.num_attention_heads   ,		intermediate_size=self.intermediate_size   ,		hidden_act=self.hidden_act   ,		hidden_dropout_prob=self.hidden_dropout_prob   ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,		max_position_embeddings=self.max_position_embeddings   ,		initializer_range=self.initializer_range   ,		)







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		int 					=					MPNetModel(config=__a       )
											model.to(__a       )
											model.eval()
											snake_case_					:		Union[str, Any] 					=					model(__a   ,		__a       )
											snake_case_					:		List[Any] 					=					model(__a       )
											self.parent.assertEqual(result.last_hidden_state.shape   ,		(self.batch_size, self.seq_length, self.hidden_size)       )
											self.parent.assertEqual(result.pooler_output.shape   ,		(self.batch_size, self.hidden_size)       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		Union[str, Any] 					=					MPNetForQuestionAnswering(config=__a       )
											model.to(__a       )
											model.eval()
											snake_case_					:		Optional[int] 					=					model(
											    __a   ,		attention_mask=__a   ,		start_positions=__a   ,		end_positions=__a   ,		)
											self.parent.assertEqual(result.start_logits.shape   ,		(self.batch_size, self.seq_length)       )
											self.parent.assertEqual(result.end_logits.shape   ,		(self.batch_size, self.seq_length)       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		List[Any] 					=					self.num_labels
											snake_case_					:		List[Any] 					=					MPNetForSequenceClassification(__a       )
											model.to(__a       )
											model.eval()
											snake_case_					:		Tuple 					=					model(__a   ,		attention_mask=__a   ,		labels=__a       )
											self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.num_labels)       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		Tuple 					=					self.num_choices
											snake_case_					:		Optional[int] 					=					MPNetForMultipleChoice(config=__a       )
											model.to(__a       )
											model.eval()
											snake_case_					:		List[str] 					=					input_ids.unsqueeze(1       ).expand(-1   ,		self.num_choices   ,		-1       ).contiguous()
											snake_case_					:		List[str] 					=					input_mask.unsqueeze(1       ).expand(-1   ,		self.num_choices   ,		-1       ).contiguous()
											snake_case_					:		List[Any] 					=					model(
											    __a   ,		attention_mask=__a   ,		labels=__a   ,		)
											self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.num_choices)       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		Dict 					=					self.num_labels
											snake_case_					:		Dict 					=					MPNetForTokenClassification(config=__a       )
											model.to(__a       )
											model.eval()
											snake_case_					:		Optional[Any] 					=					model(__a   ,		attention_mask=__a   ,		labels=__a       )
											self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.seq_length, self.num_labels)       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		str 					=					self.prepare_config_and_inputs()
											((snake_case_)					,			(snake_case_)					,			(snake_case_)					,			(snake_case_)					,			(snake_case_)					,			(snake_case_))					:		List[Any] 					=					config_and_inputs
											snake_case_					:		Optional[int] 					=					{"""input_ids""": input_ids, """attention_mask""": input_mask}
											return config, inputs_dict







@require_torch
class    __lowercase     (					_UpperCAmelCase					,    _UpperCAmelCase					,    unittest.TestCase):







						"""simple docstring"""







						_A		:							str							       =		(
						    (
						        MPNetForMaskedLM,
						        MPNetForMultipleChoice,
						        MPNetForQuestionAnswering,
						        MPNetForSequenceClassification,
						        MPNetForTokenClassification,
						        MPNetModel,
						    )
						    if is_torch_available()
						    else ()
						)
						_A		:							Optional[int]							       =		(
						    {
						        """feature-extraction""": MPNetModel,
						        """fill-mask""": MPNetForMaskedLM,
						        """question-answering""": MPNetForQuestionAnswering,
						        """text-classification""": MPNetForSequenceClassification,
						        """token-classification""": MPNetForTokenClassification,
						        """zero-shot""": MPNetForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_A		:							List[Any]							       =		False
						_A		:							str							       =		True







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Union[str, Any] 					=					MPNetModelTester(self       )
											snake_case_					:		Tuple 					=					ConfigTester(self   ,		config_class=__a   ,		hidden_size=37       )







						def 						__UpperCamelCase							(self       ):
											self.config_tester.run_common_tests()







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		int 					=					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_model(*__a       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		int 					=					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_for_sequence_classification(*__a       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		str 					=					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_for_multiple_choice(*__a       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Union[str, Any] 					=					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_for_token_classification(*__a       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[Any] 					=					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_for_question_answering(*__a       )







@require_torch
class    __lowercase     (					unittest.TestCase):







						"""simple docstring"""







						@slow
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		int 					=					MPNetModel.from_pretrained("""microsoft/mpnet-base"""       )
											snake_case_					:		List[Any] 					=					torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]       )
											snake_case_					:		Dict 					=					model(__a       )[0]
											snake_case_					:		Optional[Any] 					=					torch.Size((1, 11, 7_68)       )
											self.assertEqual(output.shape   ,		__a       )
											snake_case_					:		int 					=					torch.tensor(
											    [[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]]       )
											# compare the actual values for a slice.
											self.assertTrue(torch.allclose(output[:, :3, :3]   ,		__a   ,		atol=1e-4       )       )










"""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)))




