

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					,						_A					,						_A=1_3					,						_A=7					,						_A=True					,						_A=True					,						_A=False					,						_A=True					,						_A=9_9					,						_A=6_4					,						_A=5					,						_A=4					,						_A=6_4					,						_A="gelu"					,						_A=0.1					,						_A=0.1					,						_A=5_1_2					,						_A=1_6					,						_A=2					,						_A=0.02					,						_A=3					,						_A=4					,						_A=None					,						):
												__lowerCAmelCase					=							parent
												__lowerCAmelCase					=							batch_size
												__lowerCAmelCase					=							seq_length
												__lowerCAmelCase					=							is_training
												__lowerCAmelCase					=							use_input_mask
												__lowerCAmelCase					=							use_token_type_ids
												__lowerCAmelCase					=							use_labels
												__lowerCAmelCase					=							vocab_size
												__lowerCAmelCase					=							hidden_size
												__lowerCAmelCase					=							num_hidden_layers
												__lowerCAmelCase					=							num_attention_heads
												__lowerCAmelCase					=							intermediate_size
												__lowerCAmelCase					=							hidden_act
												__lowerCAmelCase					=							hidden_dropout_prob
												__lowerCAmelCase					=							attention_probs_dropout_prob
												__lowerCAmelCase					=							max_position_embeddings
												__lowerCAmelCase					=							type_vocab_size
												__lowerCAmelCase					=							type_sequence_label_size
												__lowerCAmelCase					=							initializer_range
												__lowerCAmelCase					=							num_labels
												__lowerCAmelCase					=							num_choices
												__lowerCAmelCase					=							scope




							def        A__  ( self							):
												return MPNetConfig.from_pretrained('microsoft/mpnet-base'							)




							def        A__  ( self							):
												__lowerCAmelCase					=							ids_tensor([self.batch_size, self.seq_length]					,						self.vocab_size							)

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

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

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




							def        A__  ( 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        A__  ( self					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							MPNetModel(config=_A							)
												model.to(_A							)
												model.eval()
												__lowerCAmelCase					=							model(_A					,						_A							)
												__lowerCAmelCase					=							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        A__  ( self					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							MPNetForQuestionAnswering(config=_A							)
												model.to(_A							)
												model.eval()
												__lowerCAmelCase					=							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        A__  ( self					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							self.num_labels
												__lowerCAmelCase					=							MPNetForSequenceClassification(_A							)
												model.to(_A							)
												model.eval()
												__lowerCAmelCase					=							model(_A					,						attention_mask=_A					,						labels=_A							)
												self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.num_labels)							)




							def        A__  ( self					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							self.num_choices
												__lowerCAmelCase					=							MPNetForMultipleChoice(config=_A							)
												model.to(_A							)
												model.eval()
												__lowerCAmelCase					=							input_ids.unsqueeze(1							).expand(-1					,						self.num_choices					,						-1							).contiguous()
												__lowerCAmelCase					=							input_mask.unsqueeze(1							).expand(-1					,						self.num_choices					,						-1							).contiguous()
												__lowerCAmelCase					=							model(
												    _A					,						attention_mask=_A					,						labels=_A					,						)
												self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.num_choices)							)




							def        A__  ( self					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							self.num_labels
												__lowerCAmelCase					=							MPNetForTokenClassification(config=_A							)
												model.to(_A							)
												model.eval()
												__lowerCAmelCase					=							model(_A					,						attention_mask=_A					,						labels=_A							)
												self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.num_labels)							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.prepare_config_and_inputs()
												((__lowerCAmelCase),	(__lowerCAmelCase),	(__lowerCAmelCase),	(__lowerCAmelCase),	(__lowerCAmelCase),	(__lowerCAmelCase))					=							config_and_inputs
												__lowerCAmelCase					=							{'input_ids': input_ids, 'attention_mask': input_mask}
												return config, inputs_dict





@require_torch
class     snake_case_			(				_a			, _a			, unittest.TestCase							):


							"""simple docstring"""





							__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        A__  ( self							):
												__lowerCAmelCase					=							MPNetModelTester(self							)
												__lowerCAmelCase					=							ConfigTester(self					,						config_class=_A					,						hidden_size=3_7							)




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




							def        A__  ( self							):
												__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mpnet_model(*_A							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mpnet_for_sequence_classification(*_A							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mpnet_for_multiple_choice(*_A							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mpnet_for_token_classification(*_A							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mpnet_for_question_answering(*_A							)





@require_torch
class     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





							@slow
							def        A__  ( self							):
												__lowerCAmelCase					=							MPNetModel.from_pretrained('microsoft/mpnet-base'							)
												__lowerCAmelCase					=							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]]							)
												__lowerCAmelCase					=							model(_A							)[0]
												__lowerCAmelCase					=							torch.Size((1, 1_1, 7_6_8)							)
												self.assertEqual(output.shape					,						_A							)
												__lowerCAmelCase					=							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							)							)









from graphs.minimum_spanning_tree_kruskal import kruskal


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							9
					__lowerCAmelCase					=							[
					    [0, 1, 4],
					    [0, 7, 8],
					    [1, 2, 8],
					    [7, 8, 7],
					    [7, 6, 1],
					    [2, 8, 2],
					    [8, 6, 6],
					    [2, 3, 7],
					    [2, 5, 4],
					    [6, 5, 2],
					    [3, 5, 14],
					    [3, 4, 9],
					    [5, 4, 10],
					    [1, 7, 11],
					]

					__lowerCAmelCase					=							kruskal(UpperCAmelCase__		,					UpperCAmelCase__      )

					__lowerCAmelCase					=							[
					    [7, 6, 1],
					    [2, 8, 2],
					    [6, 5, 2],
					    [0, 1, 4],
					    [2, 5, 4],
					    [2, 3, 7],
					    [0, 7, 8],
					    [3, 4, 9],
					]

					assert sorted(UpperCAmelCase__      ) == sorted(UpperCAmelCase__      )






