code: # coding=utf-8
  # Copyright 2020 The HuggingFace Inc. team, Microsoft Corporation.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  
  import unittest
  
  from transformers import 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
  
  
  if is_torch_available():
      import torch
  
      from transformers import (
          MPNetConfig,
          MPNetForMaskedLM,
          MPNetForMultipleChoice,
          MPNetForQuestionAnswering,
          MPNetForSequenceClassification,
          MPNetForTokenClassification,
          MPNetModel,
      )
  
  
  class MPNetModelTester:
      def __init__(
          self,
          parent,
          batch_size=13,
          seq_length=7,
          is_training=True,
          use_input_mask=True,
          use_token_type_ids=False,
          use_labels=True,
          vocab_size=99,
          hidden_size=64,
          num_hidden_layers=5,
          num_attention_heads=4,
          intermediate_size=64,
          hidden_act="gelu",
          hidden_dropout_prob=0.1,
          attention_probs_dropout_prob=0.1,
          max_position_embeddings=512,
          type_vocab_size=16,
          type_sequence_label_size=2,
          initializer_range=0.02,
          num_labels=3,
          num_choices=4,
          scope=None,
      ):
          self.parent = parent
          self.batch_size = batch_size
          self.seq_length = seq_length
          self.is_training = is_training
          self.use_input_mask = use_input_mask
          self.use_token_type_ids = use_token_type_ids
          self.use_labels = use_labels
          self.vocab_size = vocab_size
          self.hidden_size = hidden_size
          self.num_hidden_layers = num_hidden_layers
          self.num_attention_heads = num_attention_heads
          self.intermediate_size = intermediate_size
          self.hidden_act = hidden_act
          self.hidden_dropout_prob = hidden_dropout_prob
          self.attention_probs_dropout_prob = attention_probs_dropout_prob
          self.max_position_embeddings = max_position_embeddings
          self.type_vocab_size = type_vocab_size
          self.type_sequence_label_size = type_sequence_label_size
          self.initializer_range = initializer_range
          self.num_labels = num_labels
          self.num_choices = num_choices
          self.scope = scope
  
      def get_large_model_config(self):
          return MPNetConfig.from_pretrained("microsoft/mpnet-base")
  
      def prepare_config_and_inputs(self):
          input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
  
          input_mask = None
          if self.use_input_mask:
              input_mask = random_attention_mask([self.batch_size, self.seq_length])
  
          sequence_labels = None
          token_labels = None
          choice_labels = None
          if self.use_labels:
              sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
              token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
              choice_labels = ids_tensor([self.batch_size], self.num_choices)
  
          config = 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,
          )
          return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
  
      def create_and_check_mpnet_model(
          self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          model = MPNetModel(config=config)
          model.to(torch_device)
          model.eval()
          result = model(input_ids, input_mask)
          result = model(input_ids)
          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 create_and_check_mpnet_for_question_answering(
          self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          model = MPNetForQuestionAnswering(config=config)
          model.to(torch_device)
          model.eval()
          result = model(
              input_ids,
              attention_mask=input_mask,
              start_positions=sequence_labels,
              end_positions=sequence_labels,
          )
          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 create_and_check_mpnet_for_sequence_classification(
          self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          config.num_labels = self.num_labels
          model = MPNetForSequenceClassification(config)
          model.to(torch_device)
          model.eval()
          result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
  
      def create_and_check_mpnet_for_multiple_choice(
          self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          config.num_choices = self.num_choices
          model = MPNetForMultipleChoice(config=config)
          model.to(torch_device)
          model.eval()
          multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
          multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
          result = model(
              multiple_choice_inputs_ids,
              attention_mask=multiple_choice_input_mask,
              labels=choice_labels,
          )
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
  
      def create_and_check_mpnet_for_token_classification(
          self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          config.num_labels = self.num_labels
          model = MPNetForTokenClassification(config=config)
          model.to(torch_device)
          model.eval()
          result = model(input_ids, attention_mask=input_mask, labels=token_labels)
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
  
      def prepare_config_and_inputs_for_common(self):
          config_and_inputs = self.prepare_config_and_inputs()
          (config, input_ids, input_mask, sequence_labels, token_labels, choice_labels) = config_and_inputs
          inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
          return config, inputs_dict
  
  
  @require_torch
  class MPNetModelTest(ModelTesterMixin, unittest.TestCase):
  
      all_model_classes = (
          (
              MPNetForMaskedLM,
              MPNetForMultipleChoice,
              MPNetForQuestionAnswering,
              MPNetForSequenceClassification,
              MPNetForTokenClassification,
              MPNetModel,
          )
          if is_torch_available()
          else ()
      )
      test_pruning = False
      test_torchscript = True
      test_resize_embeddings = True
  
      def setUp(self):
          self.model_tester = MPNetModelTester(self)
          self.config_tester = ConfigTester(self, config_class=MPNetConfig, hidden_size=37)
  
      def test_config(self):
          self.config_tester.run_common_tests()
  
      def test_mpnet_model(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_model(*config_and_inputs)
  
      def test_for_sequence_classification(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_sequence_classification(*config_and_inputs)
  
      def test_for_multiple_choice(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_multiple_choice(*config_and_inputs)
  
      def test_for_token_classification(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_token_classification(*config_and_inputs)
  
      def test_for_question_answering(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_question_answering(*config_and_inputs)
  
  
  @require_torch
  class MPNetModelIntegrationTest(unittest.TestCase):
      @slow
      def test_inference_no_head(self):
          model = MPNetModel.from_pretrained("microsoft/mpnet-base")
          input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
          output = model(input_ids)[0]
          expected_shape = torch.Size((1, 11, 768))
          self.assertEqual(output.shape, expected_shape)
          expected_slice = 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], expected_slice, atol=1e-4))
  