text: # coding=utf-8
  # Copyright 2018 The Google AI Language Team Authors.
  #
  # 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 ElectraConfig, 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.modeling_tf_electra import (
          TFElectraForMaskedLM,
          TFElectraForMultipleChoice,
          TFElectraForPreTraining,
          TFElectraForQuestionAnswering,
          TFElectraForSequenceClassification,
          TFElectraForTokenClassification,
          TFElectraModel,
      )
  
  
  class TFElectraModelTester:
      def __init__(
          self,
          parent,
      ):
          self.parent = parent
          self.batch_size = 13
          self.seq_length = 7
          self.is_training = True
          self.use_input_mask = True
          self.use_token_type_ids = True
          self.use_labels = True
          self.vocab_size = 99
          self.hidden_size = 32
          self.num_hidden_layers = 5
          self.num_attention_heads = 4
          self.intermediate_size = 37
          self.hidden_act = "gelu"
          self.hidden_dropout_prob = 0.1
          self.attention_probs_dropout_prob = 0.1
          self.max_position_embeddings = 512
          self.type_vocab_size = 16
          self.type_sequence_label_size = 2
          self.initializer_range = 0.02
          self.num_labels = 3
          self.num_choices = 4
          self.scope = None
          self.embedding_size = 128
  
      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)
  
          token_type_ids = None
          if self.use_token_type_ids:
              token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
  
          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 = ElectraConfig(
              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,
              type_vocab_size=self.type_vocab_size,
              initializer_range=self.initializer_range,
              return_dict=True,
          )
  
          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
  
      def create_and_check_electra_model(
          self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          model = TFElectraModel(config=config)
          inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          result = model(inputs)
  
          inputs = [input_ids, input_mask]
          result = model(inputs)
  
          result = model(input_ids)
  
          self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
  
      def create_and_check_electra_for_masked_lm(
          self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          model = TFElectraForMaskedLM(config=config)
          inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          result = model(inputs)
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
  
      def create_and_check_electra_for_pretraining(
          self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          model = TFElectraForPreTraining(config=config)
          inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          result = model(inputs)
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length))
  
      def create_and_check_electra_for_sequence_classification(
          self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          config.num_labels = self.num_labels
          model = TFElectraForSequenceClassification(config=config)
          inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          result = model(inputs)
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
  
      def create_and_check_electra_for_multiple_choice(
          self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          config.num_choices = self.num_choices
          model = TFElectraForMultipleChoice(config=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))
          multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
          inputs = {
              "input_ids": multiple_choice_inputs_ids,
              "attention_mask": multiple_choice_input_mask,
              "token_type_ids": multiple_choice_token_type_ids,
          }
          result = model(inputs)
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
  
      def create_and_check_electra_for_question_answering(
          self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          model = TFElectraForQuestionAnswering(config=config)
          inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          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_electra_for_token_classification(
          self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          config.num_labels = self.num_labels
          model = TFElectraForTokenClassification(config=config)
          inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          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,
              token_type_ids,
              input_mask,
              sequence_labels,
              token_labels,
              choice_labels,
          ) = config_and_inputs
          inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
          return config, inputs_dict
  
  
  @require_tf
  class TFElectraModelTest(TFModelTesterMixin, unittest.TestCase):
  
      all_model_classes = (
          (
              TFElectraModel,
              TFElectraForMaskedLM,
              TFElectraForPreTraining,
              TFElectraForTokenClassification,
              TFElectraForMultipleChoice,
              TFElectraForSequenceClassification,
              TFElectraForQuestionAnswering,
          )
          if is_tf_available()
          else ()
      )
  
      def setUp(self):
          self.model_tester = TFElectraModelTester(self)
          self.config_tester = ConfigTester(self, config_class=ElectraConfig, hidden_size=37)
  
      def test_config(self):
          self.config_tester.run_common_tests()
  
      def test_electra_model(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_electra_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_electra_for_masked_lm(*config_and_inputs)
  
      def test_for_pretraining(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_electra_for_pretraining(*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_electra_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_electra_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_electra_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_electra_for_token_classification(*config_and_inputs)
  
      @slow
      def test_model_from_pretrained(self):
          # for model_name in TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          for model_name in ["google/electra-small-discriminator"]:
              model = TFElectraModel.from_pretrained(model_name)
              self.assertIsNotNone(model)
  