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.
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import unittest
  import shutil
  import pytest
  
  from pytorch_transformers import (DistilBertConfig, DistilBertModel, DistilBertForMaskedLM,
                                    DistilBertForQuestionAnswering, DistilBertForSequenceClassification)
  from pytorch_transformers.modeling_distilbert import DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP
  
  from .modeling_common_test import (CommonTestCases, ConfigTester, ids_tensor)
  
  
  class DistilBertModelTest(CommonTestCases.CommonModelTester):
  
      all_model_classes = (DistilBertModel, DistilBertForMaskedLM, DistilBertForQuestionAnswering,
                           DistilBertForSequenceClassification)
      test_pruning = True
      test_torchscript = True
      test_resize_embeddings = True
      test_head_masking = True
  
      class DistilBertModelTester(object):
  
          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=32,
                       num_hidden_layers=5,
                       num_attention_heads=4,
                       intermediate_size=37,
                       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 = DistilBertConfig(
                  vocab_size_or_config_json_file=self.vocab_size,
                  dim=self.hidden_size,
                  n_layers=self.num_hidden_layers,
                  n_heads=self.num_attention_heads,
                  hidden_dim=self.intermediate_size,
                  hidden_act=self.hidden_act,
                  dropout=self.hidden_dropout_prob,
                  attention_dropout=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 check_loss_output(self, result):
              self.parent.assertListEqual(
                  list(result["loss"].size()),
                  [])
  
          def create_and_check_distilbert_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
              model = DistilBertModel(config=config)
              model.eval()
              (sequence_output,) = model(input_ids, input_mask)
              (sequence_output,) = model(input_ids)
  
              result = {
                  "sequence_output": sequence_output,
              }
              self.parent.assertListEqual(
                  list(result["sequence_output"].size()),
                  [self.batch_size, self.seq_length, self.hidden_size])
  
          def create_and_check_distilbert_for_masked_lm(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
              model = DistilBertForMaskedLM(config=config)
              model.eval()
              loss, prediction_scores = model(input_ids, attention_mask=input_mask, masked_lm_labels=token_labels)
              result = {
                  "loss": loss,
                  "prediction_scores": prediction_scores,
              }
              self.parent.assertListEqual(
                  list(result["prediction_scores"].size()),
                  [self.batch_size, self.seq_length, self.vocab_size])
              self.check_loss_output(result)
  
          def create_and_check_distilbert_for_question_answering(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
              model = DistilBertForQuestionAnswering(config=config)
              model.eval()
              loss, start_logits, end_logits = model(input_ids, input_mask, sequence_labels, sequence_labels)
              result = {
                  "loss": loss,
                  "start_logits": start_logits,
                  "end_logits": end_logits,
              }
              self.parent.assertListEqual(
                  list(result["start_logits"].size()),
                  [self.batch_size, self.seq_length])
              self.parent.assertListEqual(
                  list(result["end_logits"].size()),
                  [self.batch_size, self.seq_length])
              self.check_loss_output(result)
  
          def create_and_check_distilbert_for_sequence_classification(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
              config.num_labels = self.num_labels
              model = DistilBertForSequenceClassification(config)
              model.eval()
              loss, logits = model(input_ids, input_mask, sequence_labels)
              result = {
                  "loss": loss,
                  "logits": logits,
              }
              self.parent.assertListEqual(
                  list(result["logits"].size()),
                  [self.batch_size, self.num_labels])
              self.check_loss_output(result)
  
          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
  
      def setUp(self):
          self.model_tester = DistilBertModelTest.DistilBertModelTester(self)
          self.config_tester = ConfigTester(self, config_class=DistilBertConfig, dim=37)
  
      def test_config(self):
          self.config_tester.run_common_tests()
  
      def test_distilbert_model(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_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_distilbert_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_distilbert_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_distilbert_for_sequence_classification(*config_and_inputs)
  
      # @pytest.mark.slow
      # def test_model_from_pretrained(self):
      #     cache_dir = "/tmp/pytorch_transformers_test/"
      #     for model_name in list(DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
      #         model = DistilBertModel.from_pretrained(model_name, cache_dir=cache_dir)
      #         shutil.rmtree(cache_dir)
      #         self.assertIsNotNone(model)
  
  if __name__ == "__main__":
      unittest.main()
  