text: # coding=utf-8
  # Copyright 2020 The HuggingFace Team. All rights reserved.
  #
  # 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 CTRLConfig, 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.ctrl.modeling_tf_ctrl import (
          TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFCTRLForSequenceClassification,
          TFCTRLLMHeadModel,
          TFCTRLModel,
      )
  
  
  class TFCTRLModelTester(object):
      def __init__(
          self,
          parent,
      ):
          self.parent = parent
          self.batch_size = 13
          self.seq_length = 7
          self.is_training = True
          self.use_token_type_ids = True
          self.use_input_mask = True
          self.use_labels = True
          self.use_mc_token_ids = 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.pad_token_id = self.vocab_size - 1
  
      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)
  
          mc_token_ids = None
          if self.use_mc_token_ids:
              mc_token_ids = ids_tensor([self.batch_size, self.num_choices], 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 = CTRLConfig(
              vocab_size=self.vocab_size,
              n_embd=self.hidden_size,
              n_layer=self.num_hidden_layers,
              n_head=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,
              n_positions=self.max_position_embeddings,
              n_ctx=self.max_position_embeddings,
              # type_vocab_size=self.type_vocab_size,
              # initializer_range=self.initializer_range,
              pad_token_id=self.pad_token_id,
          )
  
          head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
  
          return (
              config,
              input_ids,
              input_mask,
              head_mask,
              token_type_ids,
              mc_token_ids,
              sequence_labels,
              token_labels,
              choice_labels,
          )
  
      def create_and_check_ctrl_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
          model = TFCTRLModel(config=config)
          inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          result = model(inputs)
  
          inputs = [input_ids, None, input_mask]  # None is the input for 'past'
          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_ctrl_lm_head(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
          model = TFCTRLLMHeadModel(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_ctrl_for_sequence_classification(
          self, config, input_ids, input_mask, head_mask, token_type_ids, *args
      ):
          config.num_labels = self.num_labels
          sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
          inputs = {
              "input_ids": input_ids,
              "token_type_ids": token_type_ids,
              "labels": sequence_labels,
          }
          model = TFCTRLForSequenceClassification(config)
          result = model(inputs)
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
  
      def prepare_config_and_inputs_for_common(self):
          config_and_inputs = self.prepare_config_and_inputs()
  
          (
              config,
              input_ids,
              input_mask,
              head_mask,
              token_type_ids,
              mc_token_ids,
              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 TFCTRLModelTest(TFModelTesterMixin, unittest.TestCase):
  
      all_model_classes = (TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
      all_generative_model_classes = (TFCTRLLMHeadModel,) if is_tf_available() else ()
      test_head_masking = False
  
      def setUp(self):
          self.model_tester = TFCTRLModelTester(self)
          self.config_tester = ConfigTester(self, config_class=CTRLConfig, n_embd=37)
  
      def test_config(self):
          self.config_tester.run_common_tests()
  
      def test_ctrl_model(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_ctrl_model(*config_and_inputs)
  
      def test_ctrl_lm_head(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_ctrl_lm_head(*config_and_inputs)
  
      def test_ctrl_sequence_classification_model(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_ctrl_for_sequence_classification(*config_and_inputs)
  
      def test_model_common_attributes(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
          list_lm_models = [TFCTRLLMHeadModel]
          list_other_models_with_output_ebd = [TFCTRLForSequenceClassification]
  
          for model_class in self.all_model_classes:
              model = model_class(config)
              assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
  
              if model_class in list_lm_models:
                  x = model.get_output_embeddings()
                  assert isinstance(x, tf.keras.layers.Layer)
                  name = model.get_bias()
                  assert isinstance(name, dict)
                  for k, v in name.items():
                      assert isinstance(v, tf.Variable)
              elif model_class in list_other_models_with_output_ebd:
                  x = model.get_output_embeddings()
                  assert isinstance(x, tf.keras.layers.Layer)
                  name = model.get_bias()
                  assert name is None
              else:
                  x = model.get_output_embeddings()
                  assert x is None
                  name = model.get_bias()
                  assert name is None
  
      @slow
      def test_model_from_pretrained(self):
          for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              model = TFCTRLModel.from_pretrained(model_name)
              self.assertIsNotNone(model)
  
  
  @require_tf
  class TFCTRLModelLanguageGenerationTest(unittest.TestCase):
      @slow
      def test_lm_generate_ctrl(self):
          model = TFCTRLLMHeadModel.from_pretrained("ctrl")
          input_ids = tf.convert_to_tensor([[11859, 0, 1611, 8]], dtype=tf.int32)  # Legal the president is
          expected_output_ids = [
              11859,
              0,
              1611,
              8,
              5,
              150,
              26449,
              2,
              19,
              348,
              469,
              3,
              2595,
              48,
              20740,
              246533,
              246533,
              19,
              30,
              5,
          ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
  
          output_ids = model.generate(input_ids, do_sample=False)
          self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
  