code: # Copyright (c) Facebook, Inc. and its affiliates.
  
  import unittest
  
  import tests.test_utils as test_utils
  import torch
  from VisualBERT.mmf.common.sample import Sample, SampleList
  from VisualBERT.mmf.models.mmbt import MMBT
  from VisualBERT.mmf.modules.encoders import (
      ImageEncoderFactory,
      ImageEncoderTypes,
      ResNet152ImageEncoder,
      TextEncoderFactory,
      TextEncoderTypes,
  )
  from VisualBERT.mmf.utils.build import build_model
  from VisualBERT.mmf.utils.configuration import Configuration
  from VisualBERT.mmf.utils.env import setup_imports
  from omegaconf import OmegaConf
  
  
  class TestMMBTTorchscript(unittest.TestCase):
      def setUp(self):
          test_utils.setup_proxy()
          setup_imports()
          model_name = "mmbt"
          args = test_utils.dummy_args(model=model_name)
          configuration = Configuration(args)
          config = configuration.get_config()
          model_config = config.model_config[model_name]
          model_config["training_head_type"] = "classification"
          model_config["num_labels"] = 2
          model_config.model = model_name
          self.finetune_model = build_model(model_config)
  
      def test_load_save_finetune_model(self):
          self.assertTrue(test_utils.verify_torchscript_models(self.finetune_model))
  
      def test_finetune_model(self):
          self.finetune_model.eval()
          test_sample = Sample()
          test_sample.input_ids = torch.randint(low=0, high=30255, size=(128,)).long()
          test_sample.input_mask = torch.ones(128).long()
          test_sample.segment_ids = torch.zeros(128).long()
          test_sample.image = torch.rand((3, 300, 300)).float()
          test_sample_list = SampleList([test_sample.copy()])
  
          with torch.no_grad():
              model_output = self.finetune_model.model(test_sample_list)
  
          test_sample_list = SampleList([test_sample])
          script_model = torch.jit.script(self.finetune_model.model)
          with torch.no_grad():
              script_output = script_model(test_sample_list)
  
          self.assertTrue(torch.equal(model_output["scores"], script_output["scores"]))
  
      def test_modal_end_token(self):
          self.finetune_model.eval()
  
          # Suppose 0 for <cls>, 1 for <pad> 2 for <sep>
          CLS = 0
          PAD = 1
          SEP = 2
          size = 128
  
          input_ids = torch.randint(low=0, high=30255, size=(size,)).long()
          input_mask = torch.ones(size).long()
  
          input_ids[0] = CLS
          length = torch.randint(low=2, high=size - 1, size=(1,))
          input_ids[length] = SEP
          input_ids[length + 1 :] = PAD
          input_mask[length + 1 :] = 0
  
          test_sample = Sample()
          test_sample.input_ids = input_ids.clone()
          test_sample.input_mask = input_mask.clone()
          test_sample.segment_ids = torch.zeros(size).long()
          test_sample.image = torch.rand((3, 300, 300)).float()
          test_sample_list = SampleList([test_sample])
  
          mmbt_base = self.finetune_model.model.bert
          with torch.no_grad():
              actual_modal_end_token = mmbt_base.extract_modal_end_token(test_sample_list)
  
          expected_modal_end_token = torch.zeros([1]).fill_(SEP).long()
          self.assertTrue(torch.equal(actual_modal_end_token, expected_modal_end_token))
          self.assertTrue(torch.equal(test_sample_list.input_ids[0, :-1], input_ids[1:]))
          self.assertTrue(
              torch.equal(test_sample_list.input_mask[0, :-1], input_mask[1:])
          )
  
  
  class TestMMBTConfig(unittest.TestCase):
      def test_mmbt_from_params(self):
          # default init
          mmbt = MMBT.from_params(
              modal_encoder=ImageEncoderFactory.Config(
                  type=ImageEncoderTypes.resnet152,
                  params=ResNet152ImageEncoder.Config(pretrained=False),
              ),
              text_encoder=TextEncoderFactory.Config(type=TextEncoderTypes.identity),
          )
  
          config = OmegaConf.structured(
              MMBT.Config(
                  modal_encoder=ImageEncoderFactory.Config(
                      type=ImageEncoderTypes.resnet152,
                      params=ResNet152ImageEncoder.Config(pretrained=False),
                  ),
                  text_encoder=TextEncoderFactory.Config(type=TextEncoderTypes.identity),
              )
          )
          self.assertIsNotNone(mmbt)
          # Make sure that the config is created from MMBT.Config
          self.assertEqual(mmbt.config, config)
  
      def test_mmbt_pretrained(self):
          test_utils.setup_proxy()
          mmbt = MMBT.from_params()
          self.assertIsNotNone(mmbt)
  
      def test_mmbt_directly_from_config(self):
          config = OmegaConf.structured(
              MMBT.Config(
                  modal_encoder=ImageEncoderFactory.Config(
                      type=ImageEncoderTypes.resnet152,
                      params=ResNet152ImageEncoder.Config(pretrained=False),
                  ),
                  text_encoder=TextEncoderFactory.Config(type=TextEncoderTypes.identity),
              )
          )
          mmbt = MMBT(config)
          self.assertIsNotNone(mmbt)
          # Make sure that the config is created from MMBT.Config
          self.assertEqual(mmbt.config, config)
  
apis: torch.jit.script torch.ones torch.randint torch.rand torch.no_grad torch.equal torch.zeros