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 MPNetConfig, is_tf_available
  from transformers.testing_utils import require_tf, slow
  
  from .test_configuration_common import ConfigTester
  from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
  
  
  if is_tf_available():
      import tensorflow as tf
  
      from transformers.models.mpnet.modeling_tf_mpnet import (
          TFMPNetForMaskedLM,
          TFMPNetForMultipleChoice,
          TFMPNetForQuestionAnswering,
          TFMPNetForSequenceClassification,
          TFMPNetForTokenClassification,
          TFMPNetModel,
      )
  
  
  class TFMPNetModelTester:
      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 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 = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
  
          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 = TFMPNetModel(config=config)
          inputs = {"input_ids": input_ids, "attention_mask": input_mask}
          result = model(inputs)
          inputs = [input_ids, input_mask]
          result = model(inputs)
          self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
  
      def create_and_check_mpnet_for_masked_lm(
          self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          model = TFMPNetForMaskedLM(config=config)
          inputs = {"input_ids": input_ids, "attention_mask": input_mask}
          result = model(inputs)
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
  
      def create_and_check_mpnet_for_question_answering(
          self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          model = TFMPNetForQuestionAnswering(config=config)
          inputs = {
              "input_ids": input_ids,
              "attention_mask": input_mask,
          }
          result = model(inputs)
          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 = TFMPNetForSequenceClassification(config)
          inputs = {"input_ids": input_ids, "attention_mask": input_mask}
          result = model(inputs)
          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 = TFMPNetForMultipleChoice(config)
          multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
          multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
          inputs = {
              "input_ids": multiple_choice_inputs_ids,
              "attention_mask": multiple_choice_input_mask,
          }
          result = model(inputs)
          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 = TFMPNetForTokenClassification(config)
          inputs = {"input_ids": input_ids, "attention_mask": input_mask}
          result = model(inputs)
          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_tf
  class TFMPNetModelTest(TFModelTesterMixin, unittest.TestCase):
  
      all_model_classes = (
          (
              TFMPNetForMaskedLM,
              TFMPNetForMultipleChoice,
              TFMPNetForQuestionAnswering,
              TFMPNetForSequenceClassification,
              TFMPNetForTokenClassification,
              TFMPNetModel,
          )
          if is_tf_available()
          else ()
      )
  
      def setUp(self):
          self.model_tester = TFMPNetModelTester(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_masked_lm(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_masked_lm(*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)
  
      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)
  
      @slow
      def test_model_from_pretrained(self):
          for model_name in ["microsoft/mpnet-base"]:
              model = TFMPNetModel.from_pretrained(model_name)
              self.assertIsNotNone(model)
  