




'''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 		a							:


				def __init__(						self       :   Tuple			,							lowercase_       :   Tuple			,							lowercase_       :   str=13			,							lowercase_       :   Dict=7			,							lowercase_       :   Dict=True			,							lowercase_       :   List[str]=True			,							lowercase_       :   Union[str, Any]=False			,							lowercase_       :   str=True			,							lowercase_       :   Dict=99			,							lowercase_       :   str=64			,							lowercase_       :   Dict=5			,							lowercase_       :   List[str]=4			,							lowercase_       :   List[str]=64			,							lowercase_       :   Any="gelu"			,							lowercase_       :   Dict=0.1			,							lowercase_       :   int=0.1			,							lowercase_       :   int=512			,							lowercase_       :   Dict=16			,							lowercase_       :   str=2			,							lowercase_       :   List[str]=0.02			,							lowercase_       :   Any=3			,							lowercase_       :   Tuple=4			,							lowercase_       :   Dict=None			,							):
									snake_case_		     =							parent
									snake_case_		     =							batch_size
									snake_case_		     =							seq_length
									snake_case_		     =							is_training
									snake_case_		     =							use_input_mask
									snake_case_		     =							use_token_type_ids
									snake_case_		     =							use_labels
									snake_case_		     =							vocab_size
									snake_case_		     =							hidden_size
									snake_case_		     =							num_hidden_layers
									snake_case_		     =							num_attention_heads
									snake_case_		     =							intermediate_size
									snake_case_		     =							hidden_act
									snake_case_		     =							hidden_dropout_prob
									snake_case_		     =							attention_probs_dropout_prob
									snake_case_		     =							max_position_embeddings
									snake_case_		     =							type_vocab_size
									snake_case_		     =							type_sequence_label_size
									snake_case_		     =							initializer_range
									snake_case_		     =							num_labels
									snake_case_		     =							num_choices
									snake_case_		     =							scope


				def     A_					(						self       :   List[Any]   ):
									return MPNetConfig.from_pretrained('''microsoft/mpnet-base'''   )


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							ids_tensor([self.batch_size, self.seq_length]			,							self.vocab_size   )

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

									snake_case_		     =							None
									snake_case_		     =							None
									snake_case_		     =							None
									if self.use_labels:
														snake_case_		     =							ids_tensor([self.batch_size]			,							self.type_sequence_label_size   )
														snake_case_		     =							ids_tensor([self.batch_size, self.seq_length]			,							self.num_labels   )
														snake_case_		     =							ids_tensor([self.batch_size]			,							self.num_choices   )

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


				def     A_					(						self       :   List[str]   ):
									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     A_					(						self       :   Any			,							lowercase_       :   Optional[Any]			,							lowercase_       :   Any			,							lowercase_       :   List[str]			,							lowercase_       :   Optional[Any]			,							lowercase_       :   List[str]			,							lowercase_       :   Tuple   ):
									snake_case_		     =							MPNetModel(config=lowercase_   )
									model.to(lowercase_   )
									model.eval()
									snake_case_		     =							model(lowercase_			,							lowercase_   )
									snake_case_		     =							model(lowercase_   )
									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     A_					(						self       :   Dict			,							lowercase_       :   Optional[int]			,							lowercase_       :   List[str]			,							lowercase_       :   List[str]			,							lowercase_       :   str			,							lowercase_       :   Optional[Any]			,							lowercase_       :   Tuple   ):
									snake_case_		     =							MPNetForQuestionAnswering(config=lowercase_   )
									model.to(lowercase_   )
									model.eval()
									snake_case_		     =							model(
									    lowercase_			,							attention_mask=lowercase_			,							start_positions=lowercase_			,							end_positions=lowercase_			,							)
									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     A_					(						self       :   Optional[Any]			,							lowercase_       :   Dict			,							lowercase_       :   int			,							lowercase_       :   Tuple			,							lowercase_       :   List[str]			,							lowercase_       :   int			,							lowercase_       :   List[str]   ):
									snake_case_		     =							self.num_labels
									snake_case_		     =							MPNetForSequenceClassification(lowercase_   )
									model.to(lowercase_   )
									model.eval()
									snake_case_		     =							model(lowercase_			,							attention_mask=lowercase_			,							labels=lowercase_   )
									self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.num_labels)   )


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


				def     A_					(						self       :   str			,							lowercase_       :   Tuple			,							lowercase_       :   str			,							lowercase_       :   Optional[Any]			,							lowercase_       :   Any			,							lowercase_       :   Optional[Any]			,							lowercase_       :   Optional[Any]   ):
									snake_case_		     =							self.num_labels
									snake_case_		     =							MPNetForTokenClassification(config=lowercase_   )
									model.to(lowercase_   )
									model.eval()
									snake_case_		     =							model(lowercase_			,							attention_mask=lowercase_			,							labels=lowercase_   )
									self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.seq_length, self.num_labels)   )


				def     A_					(						self       :   Optional[int]   ):
									snake_case_		     =							self.prepare_config_and_inputs()
									((snake_case_)     ,(snake_case_)     ,(snake_case_)     ,(snake_case_)     ,(snake_case_)     ,(snake_case_))		     =							config_and_inputs
									snake_case_		     =							{'''input_ids''': input_ids, '''attention_mask''': input_mask}
									return config, inputs_dict







@require_torch
class 		a							(  _lowerCamelCase  ,	_lowerCamelCase  ,	unittest.TestCase	):
				snake_case_           =						(
				    (
				        MPNetForMaskedLM,
				        MPNetForMultipleChoice,
				        MPNetForQuestionAnswering,
				        MPNetForSequenceClassification,
				        MPNetForTokenClassification,
				        MPNetModel,
				    )
				    if is_torch_available()
				    else ()
				)
				snake_case_           =						(
				    {
				        "feature-extraction": MPNetModel,
				        "fill-mask": MPNetForMaskedLM,
				        "question-answering": MPNetForQuestionAnswering,
				        "text-classification": MPNetForSequenceClassification,
				        "token-classification": MPNetForTokenClassification,
				        "zero-shot": MPNetForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				snake_case_           =						False
				snake_case_           =						True


				def     A_					(						self       :   List[str]   ):
									snake_case_		     =							MPNetModelTester(self   )
									snake_case_		     =							ConfigTester(self			,							config_class=lowercase_			,							hidden_size=37   )


				def     A_					(						self       :   Tuple   ):
									self.config_tester.run_common_tests()


				def     A_					(						self       :   Optional[int]   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_model(*lowercase_   )


				def     A_					(						self       :   Dict   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowercase_   )


				def     A_					(						self       :   Any   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowercase_   )


				def     A_					(						self       :   str   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_token_classification(*lowercase_   )


				def     A_					(						self       :   List[str]   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_question_answering(*lowercase_   )







@require_torch
class 		a							(  unittest.TestCase	):


				@slow
				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							MPNetModel.from_pretrained('''microsoft/mpnet-base'''   )
									snake_case_		     =							torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]   )
									snake_case_		     =							model(lowercase_   )[0]
									snake_case_		     =							torch.Size((1, 11, 768)   )
									self.assertEqual(output.shape			,							lowercase_   )
									snake_case_		     =							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]			,							lowercase_			,							atol=1e-4   )   )







'''simple docstring'''

from numpy import exp, pi, sqrt


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0      )						->   int:

					'''simple docstring'''

					return 1 / sqrt(2 * pi * sigma**2      ) * exp(-((x - mu) ** 2) / (2 * sigma**2)      )


if __name__ == "__main__":
							import doctest

							doctest.testmod()

