text: # coding=utf-8
  # Copyright 2021 The HuggingFace Inc. 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.
  """ Testing suite for the PyTorch LUKE model. """
  import unittest
  
  from transformers import LukeConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  
  from ..test_configuration_common import ConfigTester
  from ..test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
  
  
  if is_torch_available():
      import torch
  
      from transformers import (
          LukeForEntityClassification,
          LukeForEntityPairClassification,
          LukeForEntitySpanClassification,
          LukeForMaskedLM,
          LukeModel,
          LukeTokenizer,
      )
      from transformers.models.luke.modeling_luke import LUKE_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  class LukeModelTester:
      def __init__(
          self,
          parent,
          batch_size=13,
          seq_length=7,
          is_training=True,
          entity_length=3,
          mention_length=5,
          use_attention_mask=True,
          use_token_type_ids=True,
          use_entity_ids=True,
          use_entity_attention_mask=True,
          use_entity_token_type_ids=True,
          use_entity_position_ids=True,
          use_labels=True,
          vocab_size=99,
          entity_vocab_size=10,
          entity_emb_size=6,
          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_entity_classification_labels=9,
          num_entity_pair_classification_labels=6,
          num_entity_span_classification_labels=4,
          use_entity_aware_attention=True,
          scope=None,
      ):
          self.parent = parent
          self.batch_size = batch_size
          self.seq_length = seq_length
          self.is_training = is_training
          self.entity_length = entity_length
          self.mention_length = mention_length
          self.use_attention_mask = use_attention_mask
          self.use_token_type_ids = use_token_type_ids
          self.use_entity_ids = use_entity_ids
          self.use_entity_attention_mask = use_entity_attention_mask
          self.use_entity_token_type_ids = use_entity_token_type_ids
          self.use_entity_position_ids = use_entity_position_ids
          self.use_labels = use_labels
          self.vocab_size = vocab_size
          self.entity_vocab_size = entity_vocab_size
          self.entity_emb_size = entity_emb_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_entity_classification_labels = num_entity_classification_labels
          self.num_entity_pair_classification_labels = num_entity_pair_classification_labels
          self.num_entity_span_classification_labels = num_entity_span_classification_labels
          self.scope = scope
          self.use_entity_aware_attention = use_entity_aware_attention
  
          self.encoder_seq_length = seq_length
          self.key_length = seq_length
          self.num_hidden_states_types = 2  # hidden_states and entity_hidden_states
  
      def prepare_config_and_inputs(self):
          # prepare words
          input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
  
          attention_mask = None
          if self.use_attention_mask:
              attention_mask = random_attention_mask([self.batch_size, self.seq_length])
  
          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)
  
          # prepare entities
          entity_ids = ids_tensor([self.batch_size, self.entity_length], self.entity_vocab_size)
  
          entity_attention_mask = None
          if self.use_entity_attention_mask:
              entity_attention_mask = random_attention_mask([self.batch_size, self.entity_length])
  
          entity_token_type_ids = None
          if self.use_token_type_ids:
              entity_token_type_ids = ids_tensor([self.batch_size, self.entity_length], self.type_vocab_size)
  
          entity_position_ids = None
          if self.use_entity_position_ids:
              entity_position_ids = ids_tensor(
                  [self.batch_size, self.entity_length, self.mention_length], self.mention_length
              )
  
          sequence_labels = None
          labels = None
          entity_labels = None
          entity_classification_labels = None
          entity_pair_classification_labels = None
          entity_span_classification_labels = None
  
          if self.use_labels:
              sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
              labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
              entity_labels = ids_tensor([self.batch_size, self.entity_length], self.entity_vocab_size)
  
              entity_classification_labels = ids_tensor([self.batch_size], self.num_entity_classification_labels)
              entity_pair_classification_labels = ids_tensor(
                  [self.batch_size], self.num_entity_pair_classification_labels
              )
              entity_span_classification_labels = ids_tensor(
                  [self.batch_size, self.entity_length], self.num_entity_span_classification_labels
              )
  
          config = self.get_config()
  
          return (
              config,
              input_ids,
              attention_mask,
              token_type_ids,
              entity_ids,
              entity_attention_mask,
              entity_token_type_ids,
              entity_position_ids,
              sequence_labels,
              labels,
              entity_labels,
              entity_classification_labels,
              entity_pair_classification_labels,
              entity_span_classification_labels,
          )
  
      def get_config(self):
          return LukeConfig(
              vocab_size=self.vocab_size,
              entity_vocab_size=self.entity_vocab_size,
              entity_emb_size=self.entity_emb_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,
              is_decoder=False,
              initializer_range=self.initializer_range,
              use_entity_aware_attention=self.use_entity_aware_attention,
          )
  
      def create_and_check_model(
          self,
          config,
          input_ids,
          attention_mask,
          token_type_ids,
          entity_ids,
          entity_attention_mask,
          entity_token_type_ids,
          entity_position_ids,
          sequence_labels,
          labels,
          entity_labels,
          entity_classification_labels,
          entity_pair_classification_labels,
          entity_span_classification_labels,
      ):
          model = LukeModel(config=config)
          model.to(torch_device)
          model.eval()
          # test with words + entities
          result = model(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              entity_ids=entity_ids,
              entity_attention_mask=entity_attention_mask,
              entity_token_type_ids=entity_token_type_ids,
              entity_position_ids=entity_position_ids,
          )
          self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
          self.parent.assertEqual(
              result.entity_last_hidden_state.shape, (self.batch_size, self.entity_length, self.hidden_size)
          )
  
          # test with words only
          result = model(input_ids, token_type_ids=token_type_ids)
          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_for_masked_lm(
          self,
          config,
          input_ids,
          attention_mask,
          token_type_ids,
          entity_ids,
          entity_attention_mask,
          entity_token_type_ids,
          entity_position_ids,
          sequence_labels,
          labels,
          entity_labels,
          entity_classification_labels,
          entity_pair_classification_labels,
          entity_span_classification_labels,
      ):
          config.num_labels = self.num_entity_classification_labels
          model = LukeForMaskedLM(config)
          model.to(torch_device)
          model.eval()
  
          result = model(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              entity_ids=entity_ids,
              entity_attention_mask=entity_attention_mask,
              entity_token_type_ids=entity_token_type_ids,
              entity_position_ids=entity_position_ids,
              labels=labels,
              entity_labels=entity_labels,
          )
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
          self.parent.assertEqual(
              result.entity_logits.shape, (self.batch_size, self.entity_length, self.entity_vocab_size)
          )
  
      def create_and_check_for_entity_classification(
          self,
          config,
          input_ids,
          attention_mask,
          token_type_ids,
          entity_ids,
          entity_attention_mask,
          entity_token_type_ids,
          entity_position_ids,
          sequence_labels,
          labels,
          entity_labels,
          entity_classification_labels,
          entity_pair_classification_labels,
          entity_span_classification_labels,
      ):
          config.num_labels = self.num_entity_classification_labels
          model = LukeForEntityClassification(config)
          model.to(torch_device)
          model.eval()
  
          result = model(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              entity_ids=entity_ids,
              entity_attention_mask=entity_attention_mask,
              entity_token_type_ids=entity_token_type_ids,
              entity_position_ids=entity_position_ids,
              labels=entity_classification_labels,
          )
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_entity_classification_labels))
  
      def create_and_check_for_entity_pair_classification(
          self,
          config,
          input_ids,
          attention_mask,
          token_type_ids,
          entity_ids,
          entity_attention_mask,
          entity_token_type_ids,
          entity_position_ids,
          sequence_labels,
          labels,
          entity_labels,
          entity_classification_labels,
          entity_pair_classification_labels,
          entity_span_classification_labels,
      ):
          config.num_labels = self.num_entity_pair_classification_labels
          model = LukeForEntityClassification(config)
          model.to(torch_device)
          model.eval()
  
          result = model(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              entity_ids=entity_ids,
              entity_attention_mask=entity_attention_mask,
              entity_token_type_ids=entity_token_type_ids,
              entity_position_ids=entity_position_ids,
              labels=entity_pair_classification_labels,
          )
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_entity_pair_classification_labels))
  
      def create_and_check_for_entity_span_classification(
          self,
          config,
          input_ids,
          attention_mask,
          token_type_ids,
          entity_ids,
          entity_attention_mask,
          entity_token_type_ids,
          entity_position_ids,
          sequence_labels,
          labels,
          entity_labels,
          entity_classification_labels,
          entity_pair_classification_labels,
          entity_span_classification_labels,
      ):
          config.num_labels = self.num_entity_span_classification_labels
          model = LukeForEntitySpanClassification(config)
          model.to(torch_device)
          model.eval()
  
          entity_start_positions = ids_tensor([self.batch_size, self.entity_length], self.seq_length)
          entity_end_positions = ids_tensor([self.batch_size, self.entity_length], self.seq_length)
  
          result = model(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              entity_ids=entity_ids,
              entity_attention_mask=entity_attention_mask,
              entity_token_type_ids=entity_token_type_ids,
              entity_position_ids=entity_position_ids,
              entity_start_positions=entity_start_positions,
              entity_end_positions=entity_end_positions,
              labels=entity_span_classification_labels,
          )
          self.parent.assertEqual(
              result.logits.shape, (self.batch_size, self.entity_length, self.num_entity_span_classification_labels)
          )
  
      def prepare_config_and_inputs_for_common(self):
          config_and_inputs = self.prepare_config_and_inputs()
          (
              config,
              input_ids,
              attention_mask,
              token_type_ids,
              entity_ids,
              entity_attention_mask,
              entity_token_type_ids,
              entity_position_ids,
              sequence_labels,
              labels,
              entity_labels,
              entity_classification_labels,
              entity_pair_classification_labels,
              entity_span_classification_labels,
          ) = config_and_inputs
          inputs_dict = {
              "input_ids": input_ids,
              "token_type_ids": token_type_ids,
              "attention_mask": attention_mask,
              "entity_ids": entity_ids,
              "entity_token_type_ids": entity_token_type_ids,
              "entity_attention_mask": entity_attention_mask,
              "entity_position_ids": entity_position_ids,
          }
          return config, inputs_dict
  
  
  @require_torch
  class LukeModelTest(ModelTesterMixin, unittest.TestCase):
  
      all_model_classes = (
          (
              LukeModel,
              LukeForMaskedLM,
              LukeForEntityClassification,
              LukeForEntityPairClassification,
              LukeForEntitySpanClassification,
          )
          if is_torch_available()
          else ()
      )
      test_pruning = False
      test_torchscript = False
      test_resize_embeddings = True
      test_head_masking = True
  
      def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
          inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
          if model_class == LukeForEntitySpanClassification:
              inputs_dict["entity_start_positions"] = torch.zeros(
                  (self.model_tester.batch_size, self.model_tester.entity_length), dtype=torch.long, device=torch_device
              )
              inputs_dict["entity_end_positions"] = torch.ones(
                  (self.model_tester.batch_size, self.model_tester.entity_length), dtype=torch.long, device=torch_device
              )
  
          if return_labels:
              if model_class in (LukeForEntityClassification, LukeForEntityPairClassification):
                  inputs_dict["labels"] = torch.zeros(
                      self.model_tester.batch_size, dtype=torch.long, device=torch_device
                  )
              elif model_class == LukeForEntitySpanClassification:
                  inputs_dict["labels"] = torch.zeros(
                      (self.model_tester.batch_size, self.model_tester.entity_length),
                      dtype=torch.long,
                      device=torch_device,
                  )
              elif model_class == LukeForMaskedLM:
                  inputs_dict["labels"] = torch.zeros(
                      (self.model_tester.batch_size, self.model_tester.seq_length),
                      dtype=torch.long,
                      device=torch_device,
                  )
                  inputs_dict["entity_labels"] = torch.zeros(
                      (self.model_tester.batch_size, self.model_tester.entity_length),
                      dtype=torch.long,
                      device=torch_device,
                  )
  
          return inputs_dict
  
      def setUp(self):
          self.model_tester = LukeModelTester(self)
          self.config_tester = ConfigTester(self, config_class=LukeConfig, hidden_size=37)
  
      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)
  
      @slow
      def test_model_from_pretrained(self):
          for model_name in LUKE_PRETRAINED_MODEL_ARCHIVE_LIST:
              model = LukeModel.from_pretrained(model_name)
              self.assertIsNotNone(model)
  
      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_entity_classification(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_entity_classification(*config_and_inputs)
  
      def test_for_entity_pair_classification(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_entity_pair_classification(*config_and_inputs)
  
      def test_for_entity_span_classification(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_entity_span_classification(*config_and_inputs)
  
      def test_attention_outputs(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
          config.return_dict = True
  
          seq_length = self.model_tester.seq_length
          entity_length = self.model_tester.entity_length
          key_length = seq_length + entity_length
  
          for model_class in self.all_model_classes:
              inputs_dict["output_attentions"] = True
              inputs_dict["output_hidden_states"] = False
              config.return_dict = True
              model = model_class(config)
              model.to(torch_device)
              model.eval()
              with torch.no_grad():
                  outputs = model(**self._prepare_for_class(inputs_dict, model_class))
              attentions = outputs.attentions
              self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
  
              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              config.output_attentions = True
              model = model_class(config)
              model.to(torch_device)
              model.eval()
              with torch.no_grad():
                  outputs = model(**self._prepare_for_class(inputs_dict, model_class))
              attentions = outputs.attentions
              self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
  
              self.assertListEqual(
                  list(attentions[0].shape[-3:]),
                  [self.model_tester.num_attention_heads, seq_length + entity_length, key_length],
              )
              out_len = len(outputs)
  
              # Check attention is always last and order is fine
              inputs_dict["output_attentions"] = True
              inputs_dict["output_hidden_states"] = True
              model = model_class(config)
              model.to(torch_device)
              model.eval()
              with torch.no_grad():
                  outputs = model(**self._prepare_for_class(inputs_dict, model_class))
  
              added_hidden_states = self.model_tester.num_hidden_states_types
              self.assertEqual(out_len + added_hidden_states, len(outputs))
  
              self_attentions = outputs.attentions
  
              self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
              self.assertListEqual(
                  list(self_attentions[0].shape[-3:]),
                  [self.model_tester.num_attention_heads, seq_length + entity_length, key_length],
              )
  
      def test_entity_hidden_states_output(self):
          def check_hidden_states_output(inputs_dict, config, model_class):
              model = model_class(config)
              model.to(torch_device)
              model.eval()
  
              with torch.no_grad():
                  outputs = model(**self._prepare_for_class(inputs_dict, model_class))
  
              entity_hidden_states = outputs.entity_hidden_states
  
              expected_num_layers = getattr(
                  self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
              )
              self.assertEqual(len(entity_hidden_states), expected_num_layers)
  
              entity_length = self.model_tester.entity_length
  
              self.assertListEqual(
                  list(entity_hidden_states[0].shape[-2:]),
                  [entity_length, self.model_tester.hidden_size],
              )
  
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              inputs_dict["output_hidden_states"] = True
              check_hidden_states_output(inputs_dict, config, model_class)
  
              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              config.output_hidden_states = True
  
              check_hidden_states_output(inputs_dict, config, model_class)
  
      def test_retain_grad_entity_hidden_states(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
          config.output_hidden_states = True
          config.output_attentions = True
  
          # no need to test all models as different heads yield the same functionality
          model_class = self.all_model_classes[0]
          model = model_class(config)
          model.to(torch_device)
  
          inputs = self._prepare_for_class(inputs_dict, model_class)
  
          outputs = model(**inputs)
  
          output = outputs[0]
  
          entity_hidden_states = outputs.entity_hidden_states[0]
          entity_hidden_states.retain_grad()
  
          output.flatten()[0].backward(retain_graph=True)
  
          self.assertIsNotNone(entity_hidden_states.grad)
  
  
  @require_torch
  class LukeModelIntegrationTests(unittest.TestCase):
      @slow
      def test_inference_base_model(self):
          model = LukeModel.from_pretrained("studio-ousia/luke-base").eval()
          model.to(torch_device)
  
          tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-base", task="entity_classification")
          text = "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the new world number one avoid a humiliating second- round exit at Wimbledon ."
          span = (39, 42)
          encoding = tokenizer(text, entity_spans=[span], add_prefix_space=True, return_tensors="pt")
  
          # move all values to device
          for key, value in encoding.items():
              encoding[key] = encoding[key].to(torch_device)
  
          outputs = model(**encoding)
  
          # Verify word hidden states
          expected_shape = torch.Size((1, 42, 768))
          self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
  
          expected_slice = torch.tensor(
              [[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]
          ).to(torch_device)
          self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))
  
          # Verify entity hidden states
          expected_shape = torch.Size((1, 1, 768))
          self.assertEqual(outputs.entity_last_hidden_state.shape, expected_shape)
  
          expected_slice = torch.tensor([[0.1457, 0.1044, 0.0174]]).to(torch_device)
          self.assertTrue(torch.allclose(outputs.entity_last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))
  
      @slow
      def test_inference_large_model(self):
          model = LukeModel.from_pretrained("studio-ousia/luke-large").eval()
          model.to(torch_device)
  
          tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-large", task="entity_classification")
          text = "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the new world number one avoid a humiliating second- round exit at Wimbledon ."
          span = (39, 42)
          encoding = tokenizer(text, entity_spans=[span], add_prefix_space=True, return_tensors="pt")
  
          # move all values to device
          for key, value in encoding.items():
              encoding[key] = encoding[key].to(torch_device)
  
          outputs = model(**encoding)
  
          # Verify word hidden states
          expected_shape = torch.Size((1, 42, 1024))
          self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
  
          expected_slice = torch.tensor(
              [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]
          ).to(torch_device)
          self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))
  
          # Verify entity hidden states
          expected_shape = torch.Size((1, 1, 1024))
          self.assertEqual(outputs.entity_last_hidden_state.shape, expected_shape)
  
          expected_slice = torch.tensor([[0.0466, -0.0106, -0.0179]]).to(torch_device)
          self.assertTrue(torch.allclose(outputs.entity_last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))
  