text: # coding=utf-8
  # Copyright 2020 HuggingFace Inc. team.
  #
  # 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 FunnelConfig, is_tf_available
  from transformers.testing_utils import require_tf
  
  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_funnel import (
          TFFunnelBaseModel,
          TFFunnelForMaskedLM,
          TFFunnelForMultipleChoice,
          TFFunnelForPreTraining,
          TFFunnelForQuestionAnswering,
          TFFunnelForSequenceClassification,
          TFFunnelForTokenClassification,
          TFFunnelModel,
      )
  
  
  class TFFunnelModelTester:
      """You can also import this e.g, from .test_modeling_funnel import FunnelModelTester """
  
      def __init__(
          self,
          parent,
          batch_size=13,
          seq_length=7,
          is_training=True,
          use_input_mask=True,
          use_token_type_ids=True,
          use_labels=True,
          vocab_size=99,
          block_sizes=[1, 1, 2],
          num_decoder_layers=1,
          d_model=32,
          n_head=4,
          d_head=8,
          d_inner=37,
          hidden_act="gelu_new",
          hidden_dropout=0.1,
          attention_dropout=0.1,
          activation_dropout=0.0,
          max_position_embeddings=512,
          type_vocab_size=3,
          num_labels=3,
          num_choices=4,
          scope=None,
          base=False,
      ):
          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.block_sizes = block_sizes
          self.num_decoder_layers = num_decoder_layers
          self.d_model = d_model
          self.n_head = n_head
          self.d_head = d_head
          self.d_inner = d_inner
          self.hidden_act = hidden_act
          self.hidden_dropout = hidden_dropout
          self.attention_dropout = attention_dropout
          self.activation_dropout = activation_dropout
          self.max_position_embeddings = max_position_embeddings
          self.type_vocab_size = type_vocab_size
          self.type_sequence_label_size = 2
          self.num_labels = num_labels
          self.num_choices = num_choices
          self.scope = scope
  
          # Used in the tests to check the size of the first attention layer
          self.num_attention_heads = n_head
          # Used in the tests to check the size of the first hidden state
          self.hidden_size = self.d_model
          # Used in the tests to check the number of output hidden states/attentions
          self.num_hidden_layers = sum(self.block_sizes) + (0 if base else self.num_decoder_layers)
          # FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
          # the last hidden state of the first block (which is the first hidden state of the decoder).
          if not base:
              self.expected_num_hidden_layers = self.num_hidden_layers + 2
  
      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 = FunnelConfig(
              vocab_size=self.vocab_size,
              block_sizes=self.block_sizes,
              num_decoder_layers=self.num_decoder_layers,
              d_model=self.d_model,
              n_head=self.n_head,
              d_head=self.d_head,
              d_inner=self.d_inner,
              hidden_act=self.hidden_act,
              hidden_dropout=self.hidden_dropout,
              attention_dropout=self.attention_dropout,
              activation_dropout=self.activation_dropout,
              max_position_embeddings=self.max_position_embeddings,
              type_vocab_size=self.type_vocab_size,
              return_dict=True,
          )
  
          return (
              config,
              input_ids,
              token_type_ids,
              input_mask,
              sequence_labels,
              token_labels,
              choice_labels,
          )
  
      def create_and_check_model(
          self,
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
      ):
          model = TFFunnelModel(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.d_model))
  
          config.truncate_seq = False
          model = TFFunnelModel(config=config)
          result = model(input_ids)
          self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
  
          config.separate_cls = False
          model = TFFunnelModel(config=config)
          result = model(input_ids)
          self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
  
      def create_and_check_base_model(
          self,
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
      ):
          model = TFFunnelBaseModel(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, 2, self.d_model))
  
          config.truncate_seq = False
          model = TFFunnelBaseModel(config=config)
          result = model(input_ids)
          self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 3, self.d_model))
  
          config.separate_cls = False
          model = TFFunnelBaseModel(config=config)
          result = model(input_ids)
          self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model))
  
      def create_and_check_for_pretraining(
          self,
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
      ):
          model = TFFunnelForPreTraining(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_for_masked_lm(
          self,
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
      ):
          model = TFFunnelForMaskedLM(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_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 = TFFunnelForSequenceClassification(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_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 = TFFunnelForMultipleChoice(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_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 = TFFunnelForTokenClassification(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 create_and_check_for_question_answering(
          self,
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
      ):
          model = TFFunnelForQuestionAnswering(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 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 TFFunnelModelTest(TFModelTesterMixin, unittest.TestCase):
      all_model_classes = (
          (
              TFFunnelModel,
              TFFunnelForMaskedLM,
              TFFunnelForPreTraining,
              TFFunnelForQuestionAnswering,
              TFFunnelForTokenClassification,
          )
          if is_tf_available()
          else ()
      )
  
      def setUp(self):
          self.model_tester = TFFunnelModelTester(self)
          self.config_tester = ConfigTester(self, config_class=FunnelConfig)
  
      def test_config(self):
          self.config_tester.run_common_tests()
  
      def test_model(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*config_and_inputs)
  
      def test_for_pretraining(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_pretraining(*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_for_masked_lm(*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_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_for_question_answering(*config_and_inputs)
  
  
  @require_tf
  class TFFunnelBaseModelTest(TFModelTesterMixin, unittest.TestCase):
      all_model_classes = (
          (TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
      )
  
      def setUp(self):
          self.model_tester = TFFunnelModelTester(self, base=True)
          self.config_tester = ConfigTester(self, config_class=FunnelConfig)
  
      def test_config(self):
          self.config_tester.run_common_tests()
  
      def test_base_model(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_base_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_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_for_multiple_choice(*config_and_inputs)
  