





"""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 		__snake_case					:





 """simple docstring"""


 def __init__(    self					,						__lowerCamelCase					,						__lowerCamelCase=13					,						__lowerCamelCase=7					,						__lowerCamelCase=True					,						__lowerCamelCase=True					,						__lowerCamelCase=False					,						__lowerCamelCase=True					,						__lowerCamelCase=99					,						__lowerCamelCase=64					,						__lowerCamelCase=5					,						__lowerCamelCase=4					,						__lowerCamelCase=64					,						__lowerCamelCase="gelu"					,						__lowerCamelCase=0.1					,						__lowerCamelCase=0.1					,						__lowerCamelCase=512					,						__lowerCamelCase=16					,						__lowerCamelCase=2					,						__lowerCamelCase=0.0_2					,						__lowerCamelCase=3					,						__lowerCamelCase=4					,						__lowerCamelCase=None					,						):



     '''simple docstring'''




     __A    :						Optional[int]						 =     parent
     __A    :						Any						 =     batch_size
     __A    :						Union[str, Any]						 =     seq_length
     __A    :						List[Any]						 =     is_training
     __A    :						Union[str, Any]						 =     use_input_mask
     __A    :						Any						 =     use_token_type_ids
     __A    :						str						 =     use_labels
     __A    :						Tuple						 =     vocab_size
     __A    :						List[str]						 =     hidden_size
     __A    :						Union[str, Any]						 =     num_hidden_layers
     __A    :						List[str]						 =     num_attention_heads
     __A    :						Optional[int]						 =     intermediate_size
     __A    :						int						 =     hidden_act
     __A    :						List[str]						 =     hidden_dropout_prob
     __A    :						Optional[Any]						 =     attention_probs_dropout_prob
     __A    :						Union[str, Any]						 =     max_position_embeddings
     __A    :						int						 =     type_vocab_size
     __A    :						str						 =     type_sequence_label_size
     __A    :						List[Any]						 =     initializer_range
     __A    :						str						 =     num_labels
     __A    :						Optional[int]						 =     num_choices
     __A    :						Dict						 =     scope

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return MPNetConfig.from_pretrained('''microsoft/mpnet-base'''  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						int						 =     ids_tensor([self.batch_size, self.seq_length]					,						self.vocab_size  )

     __A    :						str						 =     None
     if self.use_input_mask:
         __A    :						int						 =     random_attention_mask([self.batch_size, self.seq_length]  )

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

     __A    :						Optional[int]						 =     self.get_config()
     return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

 def 					UpperCamelCase__(    self  ):



     '''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 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Tuple						 =     MPNetModel(config=__lowerCamelCase  )
     model.to(__lowerCamelCase  )
     model.eval()
     __A    :						Any						 =     model(__lowerCamelCase					,						__lowerCamelCase  )
     __A    :						List[Any]						 =     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 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Any						 =     MPNetForQuestionAnswering(config=__lowerCamelCase  )
     model.to(__lowerCamelCase  )
     model.eval()
     __A    :						Union[str, Any]						 =     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 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Tuple						 =     self.num_labels
     __A    :						Tuple						 =     MPNetForSequenceClassification(__lowerCamelCase  )
     model.to(__lowerCamelCase  )
     model.eval()
     __A    :						Any						 =     model(__lowerCamelCase					,						attention_mask=__lowerCamelCase					,						labels=__lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.num_labels)  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




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

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Dict						 =     self.num_labels
     __A    :						Union[str, Any]						 =     MPNetForTokenClassification(config=__lowerCamelCase  )
     model.to(__lowerCamelCase  )
     model.eval()
     __A    :						Union[str, Any]						 =     model(__lowerCamelCase					,						attention_mask=__lowerCamelCase					,						labels=__lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.num_labels)  )







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						str						 =     self.prepare_config_and_inputs()
     ((__A) ,       (__A) ,       (__A) ,       (__A) ,       (__A) ,       (__A))    :						Dict						 =     config_and_inputs
     __A    :						int						 =     {'''input_ids''': input_ids, '''attention_mask''': input_mask}
     return config, inputs_dict






@require_torch
class 		__snake_case					(		SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__							,							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 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Optional[Any]						 =     MPNetModelTester(self  )
     __A    :						int						 =     ConfigTester(self					,						config_class=__lowerCamelCase					,						hidden_size=37  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     self.config_tester.run_common_tests()

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[str]						 =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_model(*__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						str						 =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_sequence_classification(*__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Dict						 =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_multiple_choice(*__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Optional[Any]						 =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_token_classification(*__lowerCamelCase  )







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Optional[Any]						 =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_question_answering(*__lowerCamelCase  )






@require_torch
class 		__snake_case					(		unittest.TestCase     ):





 """simple docstring"""


 @slow
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[Any]						 =     MPNetModel.from_pretrained('''microsoft/mpnet-base'''  )
     __A    :						Union[str, Any]						 =     torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]  )
     __A    :						str						 =     model(__lowerCamelCase  )[0]
     __A    :						int						 =     torch.Size((1, 11, 768)  )
     self.assertEqual(output.shape					,						__lowerCamelCase  )
     __A    :						Tuple						 =     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"""



def 		__lowercase     (   snake_case_  :  dict						)   ->set:



    '''simple docstring'''







    __A    :						List[str]						 =     set()
    # edges = list of graph's edges
    __A    :						Optional[int]						 =     get_edges(snake_case_						)

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        __A ,       __A    :						str						 =     edges.pop()
        chosen_vertices.add(snake_case_						)
        chosen_vertices.add(snake_case_						)
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(snake_case_						)
    return chosen_vertices







def 		__lowercase     (   snake_case_  :  dict						)   ->set:



    '''simple docstring'''







    __A    :						Tuple						 =     set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node)						)
    return edges


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
       # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")




