





"""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 					_UpperCAmelCase   :


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


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


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  ids_tensor([self.batch_size, self.seq_length]     ,       self.vocab_size		)

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

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

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


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


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


					def 	lowerCamelCase       (					self			:str     ,       __UpperCamelCase			:Optional[int]     ,       __UpperCamelCase			:int     ,       __UpperCamelCase			:Tuple     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:Any     ,       __UpperCamelCase			:Optional[int]		):
												A		=  self.num_labels
												A		=  MPNetForTokenClassification(config=__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()
												A		=  model(__UpperCamelCase     ,       attention_mask=__UpperCamelCase     ,       labels=__UpperCamelCase		)
												self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.seq_length, self.num_labels)		)


					def 	lowerCamelCase       (					self			:Optional[int]		):
												A		=  self.prepare_config_and_inputs()
												((A),	(A),	(A),	(A),	(A),	(A))		=  config_and_inputs
												A		=  {"input_ids": input_ids, "attention_mask": input_mask}
												return config, inputs_dict







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


					def 	lowerCamelCase       (					self			:Tuple		):
												A		=  MPNetModelTester(self		)
												A		=  ConfigTester(self     ,       config_class=__UpperCamelCase     ,       hidden_size=37		)


					def 	lowerCamelCase       (					self			:Optional[int]		):
												self.config_tester.run_common_tests()


					def 	lowerCamelCase       (					self			:Tuple		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mpnet_model(*__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mpnet_for_sequence_classification(*__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:List[str]		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mpnet_for_multiple_choice(*__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:int		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mpnet_for_token_classification(*__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:str		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mpnet_for_question_answering(*__UpperCamelCase		)







@require_torch
class 					_UpperCAmelCase   (	unittest.TestCase ):


					@slow
					def 	lowerCamelCase       (					self			:List[Any]		):
												A		=  MPNetModel.from_pretrained("microsoft/mpnet-base"		)
												A		=  torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]		)
												A		=  model(__UpperCamelCase		)[0]
												A		=  torch.Size((1, 11, 7_68)		)
												self.assertEqual(output.shape     ,       __UpperCamelCase		)
												A		=  torch.tensor(
												    [[[-0.0_550, 0.1_943, -0.0_740], [-0.0_562, 0.2_211, -0.0_579], [-0.0_437, 0.3_337, -0.0_641]]]		)
												# compare the actual values for a slice.
												self.assertTrue(torch.allclose(output[:, :3, :3]     ,       __UpperCamelCase     ,       atol=1e-4		)		)









"""simple docstring"""
from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)






class 					_UpperCAmelCase   (	lowercase_ ):


					def __init__(					self			:int     ,       __UpperCamelCase			:Distribution     ,       __UpperCamelCase			:Dict=None     ,       __UpperCamelCase			:Optional[int]=None     ,       __UpperCamelCase			:List[str]=0		):
												A		=  1.0 if scale is None else scale
												A		=  0.0 if loc is None else loc

												super().__init__(__UpperCamelCase     ,       [AffineTransform(loc=self.loc     ,       scale=self.scale     ,       event_dim=__UpperCamelCase		)]		)


					@property
					def 	lowerCamelCase       (					self			:Any		):
												return self.base_dist.mean * self.scale + self.loc


					@property
					def 	lowerCamelCase       (					self			:Optional[int]		):
												return self.base_dist.variance * self.scale**2


					@property
					def 	lowerCamelCase       (					self			:Dict		):
												return self.variance.sqrt()







class 					_UpperCAmelCase   (	nn.Module ):


					def __init__(					self			:Dict     ,       __UpperCamelCase			:int     ,       __UpperCamelCase			:Dict[str, int]     ,       __UpperCamelCase			:Callable[..., Tuple[torch.Tensor]]     ,       **__UpperCamelCase			:str		):
												super().__init__(**__UpperCamelCase		)
												A		=  args_dim
												A		=  nn.ModuleList([nn.Linear(__UpperCamelCase     ,       __UpperCamelCase		) for dim in args_dim.values()]		)
												A		=  domain_map


					def 	lowerCamelCase       (					self			:int     ,       __UpperCamelCase			:torch.Tensor		):
												A		=  [proj(__UpperCamelCase		) for proj in self.proj]

												return self.domain_map(*__UpperCamelCase		)







class 					_UpperCAmelCase   (	nn.Module ):


					def __init__(					self			:Dict     ,       __UpperCamelCase			:int		):
												super().__init__()
												A		=  function


					def 	lowerCamelCase       (					self			:List[str]     ,       __UpperCamelCase			:Any     ,       *__UpperCamelCase			:Any		):
												return self.function(__UpperCamelCase     ,       *__UpperCamelCase		)







class 					_UpperCAmelCase   :
					UpperCamelCase      =							42
					UpperCamelCase      =							42
					UpperCamelCase      =							42


					def __init__(					self			:Any     ,       __UpperCamelCase			:int = 1		):
												A		=  dim
												A		=  {k: dim * self.args_dim[k] for k in self.args_dim}


					def 	lowerCamelCase       (					self			:List[Any]     ,       __UpperCamelCase			:Dict		):
												if self.dim == 1:
																			return self.distribution_class(*__UpperCamelCase		)
												else:
																			return Independent(self.distribution_class(*__UpperCamelCase		)     ,       1		)


					def 	lowerCamelCase       (					self			:int     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:Optional[torch.Tensor] = None     ,       __UpperCamelCase			:Optional[torch.Tensor] = None     ,       ):
												A		=  self._base_distribution(__UpperCamelCase		)
												if loc is None and scale is None:
																			return distr
												else:
																			return AffineTransformed(__UpperCamelCase     ,       loc=__UpperCamelCase     ,       scale=__UpperCamelCase     ,       event_dim=self.event_dim		)


					@property
					def 	lowerCamelCase       (					self			:List[Any]		):
												return () if self.dim == 1 else (self.dim,)


					@property
					def 	lowerCamelCase       (					self			:Tuple		):
												return len(self.event_shape		)


					@property
					def 	lowerCamelCase       (					self			:int		):
												return 0.0


					def 	lowerCamelCase       (					self			:str     ,       __UpperCamelCase			:int		):
												return ParameterProjection(
												    in_features=__UpperCamelCase     ,       args_dim=self.args_dim     ,       domain_map=LambdaLayer(self.domain_map		)     ,       )


					def 	lowerCamelCase       (					self			:List[Any]     ,       *__UpperCamelCase			:torch.Tensor		):
												raise NotImplementedError()


					@staticmethod
					def 	lowerCamelCase       (					__UpperCamelCase			:torch.Tensor		):
												return (x + torch.sqrt(torch.square(__UpperCamelCase		) + 4.0		)) / 2.0







class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							{"df": 1, "loc": 1, "scale": 1}
					UpperCamelCase      =							StudentT


					@classmethod
					def 	lowerCamelCase       (					cls			:List[str]     ,       __UpperCamelCase			:torch.Tensor     ,       __UpperCamelCase			:torch.Tensor     ,       __UpperCamelCase			:torch.Tensor		):
												A		=  cls.squareplus(__UpperCamelCase		).clamp_min(torch.finfo(scale.dtype		).eps		)
												A		=  2.0 + cls.squareplus(__UpperCamelCase		)
												return df.squeeze(-1		), loc.squeeze(-1		), scale.squeeze(-1		)







class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							{"loc": 1, "scale": 1}
					UpperCamelCase      =							Normal


					@classmethod
					def 	lowerCamelCase       (					cls			:List[Any]     ,       __UpperCamelCase			:torch.Tensor     ,       __UpperCamelCase			:torch.Tensor		):
												A		=  cls.squareplus(__UpperCamelCase		).clamp_min(torch.finfo(scale.dtype		).eps		)
												return loc.squeeze(-1		), scale.squeeze(-1		)







class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							{"total_count": 1, "logits": 1}
					UpperCamelCase      =							NegativeBinomial


					@classmethod
					def 	lowerCamelCase       (					cls			:str     ,       __UpperCamelCase			:torch.Tensor     ,       __UpperCamelCase			:torch.Tensor		):
												A		=  cls.squareplus(__UpperCamelCase		)
												return total_count.squeeze(-1		), logits.squeeze(-1		)


					def 	lowerCamelCase       (					self			:Tuple     ,       __UpperCamelCase			:List[str]		):
												A,	A		=  distr_args
												if self.dim == 1:
																			return self.distribution_class(total_count=__UpperCamelCase     ,       logits=__UpperCamelCase		)
												else:
																			return Independent(self.distribution_class(total_count=__UpperCamelCase     ,       logits=__UpperCamelCase		)     ,       1		)


					def 	lowerCamelCase       (					self			:List[str]     ,       __UpperCamelCase			:str     ,       __UpperCamelCase			:Optional[torch.Tensor] = None     ,       __UpperCamelCase			:Optional[torch.Tensor] = None		):
												A,	A		=  distr_args

												if scale is not None:
																			# See scaling property of Gamma.
																			logits += scale.log()

												return self._base_distribution((total_count, logits)		)


