




'''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 :     Tuple  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     Optional[Any]=1_3  ,				__lowerCamelCase :     Dict=7  ,				__lowerCamelCase :     Tuple=True  ,				__lowerCamelCase :     int=True  ,				__lowerCamelCase :     Any=False  ,				__lowerCamelCase :     Optional[Any]=True  ,				__lowerCamelCase :     Tuple=9_9  ,				__lowerCamelCase :     Optional[int]=6_4  ,				__lowerCamelCase :     Tuple=5  ,				__lowerCamelCase :     List[Any]=4  ,				__lowerCamelCase :     str=6_4  ,				__lowerCamelCase :     Union[str, Any]="gelu"  ,				__lowerCamelCase :     List[Any]=0.1  ,				__lowerCamelCase :     List[Any]=0.1  ,				__lowerCamelCase :     Dict=5_1_2  ,				__lowerCamelCase :     List[Any]=1_6  ,				__lowerCamelCase :     Optional[int]=2  ,				__lowerCamelCase :     Optional[Any]=0.0_2  ,				__lowerCamelCase :     Optional[Any]=3  ,				__lowerCamelCase :     List[str]=4  ,				__lowerCamelCase :     str=None  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				parent
      _SCREAMING_SNAKE_CASE	=				batch_size
      _SCREAMING_SNAKE_CASE	=				seq_length
      _SCREAMING_SNAKE_CASE	=				is_training
      _SCREAMING_SNAKE_CASE	=				use_input_mask
      _SCREAMING_SNAKE_CASE	=				use_token_type_ids
      _SCREAMING_SNAKE_CASE	=				use_labels
      _SCREAMING_SNAKE_CASE	=				vocab_size
      _SCREAMING_SNAKE_CASE	=				hidden_size
      _SCREAMING_SNAKE_CASE	=				num_hidden_layers
      _SCREAMING_SNAKE_CASE	=				num_attention_heads
      _SCREAMING_SNAKE_CASE	=				intermediate_size
      _SCREAMING_SNAKE_CASE	=				hidden_act
      _SCREAMING_SNAKE_CASE	=				hidden_dropout_prob
      _SCREAMING_SNAKE_CASE	=				attention_probs_dropout_prob
      _SCREAMING_SNAKE_CASE	=				max_position_embeddings
      _SCREAMING_SNAKE_CASE	=				type_vocab_size
      _SCREAMING_SNAKE_CASE	=				type_sequence_label_size
      _SCREAMING_SNAKE_CASE	=				initializer_range
      _SCREAMING_SNAKE_CASE	=				num_labels
      _SCREAMING_SNAKE_CASE	=				num_choices
      _SCREAMING_SNAKE_CASE	=				scope



   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      return MPNetConfig.from_pretrained("microsoft/mpnet-base"   )



   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				ids_tensor([self.batch_size, self.seq_length]  ,				self.vocab_size   )

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

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

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



   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      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 :     List[Any]  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				MPNetModel(config=__lowerCamelCase   )
      model.to(__lowerCamelCase   )
      model.eval()
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase  ,				__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase   )
      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 :     Any  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				MPNetForQuestionAnswering(config=__lowerCamelCase   )
      model.to(__lowerCamelCase   )
      model.eval()
      _SCREAMING_SNAKE_CASE	=				model(
          __lowerCamelCase  ,				attention_mask=__lowerCamelCase  ,				start_positions=__lowerCamelCase  ,				end_positions=__lowerCamelCase  ,				)
      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 :     Dict  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.num_labels
      _SCREAMING_SNAKE_CASE	=				MPNetForSequenceClassification(__lowerCamelCase   )
      model.to(__lowerCamelCase   )
      model.eval()
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase  ,				attention_mask=__lowerCamelCase  ,				labels=__lowerCamelCase   )
      self.parent.assertEqual(result.logits.shape  ,				(self.batch_size, self.num_labels)   )



   def 							lowerCAmelCase_      (   self :     Optional[int]  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.num_choices
      _SCREAMING_SNAKE_CASE	=				MPNetForMultipleChoice(config=__lowerCamelCase   )
      model.to(__lowerCamelCase   )
      model.eval()
      _SCREAMING_SNAKE_CASE	=				input_ids.unsqueeze(1   ).expand(-1  ,				self.num_choices  ,				-1   ).contiguous()
      _SCREAMING_SNAKE_CASE	=				input_mask.unsqueeze(1   ).expand(-1  ,				self.num_choices  ,				-1   ).contiguous()
      _SCREAMING_SNAKE_CASE	=				model(
          __lowerCamelCase  ,				attention_mask=__lowerCamelCase  ,				labels=__lowerCamelCase  ,				)
      self.parent.assertEqual(result.logits.shape  ,				(self.batch_size, self.num_choices)   )



   def 							lowerCAmelCase_      (   self :     Optional[int]  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.num_labels
      _SCREAMING_SNAKE_CASE	=				MPNetForTokenClassification(config=__lowerCamelCase   )
      model.to(__lowerCamelCase   )
      model.eval()
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase  ,				attention_mask=__lowerCamelCase  ,				labels=__lowerCamelCase   )
      self.parent.assertEqual(result.logits.shape  ,				(self.batch_size, self.seq_length, self.num_labels)   )



   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.prepare_config_and_inputs()
      ((_SCREAMING_SNAKE_CASE),	(_SCREAMING_SNAKE_CASE),	(_SCREAMING_SNAKE_CASE),	(_SCREAMING_SNAKE_CASE),	(_SCREAMING_SNAKE_CASE),	(_SCREAMING_SNAKE_CASE))	=				config_and_inputs
      _SCREAMING_SNAKE_CASE	=				{"input_ids": input_ids, "attention_mask": input_mask}
      return config, inputs_dict







@require_torch
class 		lowercase_   (			A					,  A					,  unittest.TestCase       ):


   """simple docstring"""

   lowerCamelCase_      =						(
       (
           MPNetForMaskedLM,
           MPNetForMultipleChoice,
           MPNetForQuestionAnswering,
           MPNetForSequenceClassification,
           MPNetForTokenClassification,
           MPNetModel,
       )
       if is_torch_available()
       else ()
   )
   lowerCamelCase_      =						(
       {
           '''feature-extraction''': MPNetModel,
           '''fill-mask''': MPNetForMaskedLM,
           '''question-answering''': MPNetForQuestionAnswering,
           '''text-classification''': MPNetForSequenceClassification,
           '''token-classification''': MPNetForTokenClassification,
           '''zero-shot''': MPNetForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   lowerCamelCase_      =						False
   lowerCamelCase_      =						True



   def 							lowerCAmelCase_      (   self :     str   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				MPNetModelTester(self   )
      _SCREAMING_SNAKE_CASE	=				ConfigTester(self  ,				config_class=__lowerCamelCase  ,				hidden_size=3_7   )



   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      self.config_tester.run_common_tests()



   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_model(*__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_sequence_classification(*__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_multiple_choice(*__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_token_classification(*__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_question_answering(*__lowerCamelCase   )







@require_torch
class 		lowercase_   (			unittest.TestCase       ):


   """simple docstring"""

   @slow
   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				MPNetModel.from_pretrained("microsoft/mpnet-base"   )
      _SCREAMING_SNAKE_CASE	=				torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]   )
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase   )[0]
      _SCREAMING_SNAKE_CASE	=				torch.Size((1, 1_1, 7_6_8)   )
      self.assertEqual(output.shape  ,				__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				torch.tensor(
          [[[-0.0_5_5_0, 0.1_9_4_3, -0.0_7_4_0], [-0.0_5_6_2, 0.2_2_1_1, -0.0_5_7_9], [-0.0_4_3_7, 0.3_3_3_7, -0.0_6_4_1]]]   )
      # compare the actual values for a slice.
      self.assertTrue(torch.allclose(output[:, :3, :3]  ,				__lowerCamelCase  ,				atol=1e-4   )   )







'''simple docstring'''



import random
def 			SCREAMING_SNAKE_CASE_			(							__A    :			int	,					__A    :			float	,					__A    :			bool = False  )			->			dict:
   _SCREAMING_SNAKE_CASE	=				{i: [] for i in range(__A  )}

   # if probability is greater or equal than 1, then generate a complete graph
   if probability >= 1:
      return complete_graph(__A  )
   # if probability is lower or equal than 0, then return a graph without edges
   if probability <= 0:
      return graph

   # for each couple of nodes, add an edge from u to v
   # if the number randomly generated is greater than probability probability
   for i in range(__A  ):
      for j in range(i + 1	,					__A  ):
         if random.random() < probability:
            graph[i].append(__A  )
            if not directed:
               # if the graph is undirected, add an edge in from j to i, either
               graph[j].append(__A  )
   return graph
def 			SCREAMING_SNAKE_CASE_			(							__A    :			int  )			->			dict:
   return {
       i: [j for j in range(__A  ) if i != j] for i in range(__A  )
   }


if __name__ == "__main__":
 import doctest

 doctest.testmod()

