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 XLMConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  
  from .test_configuration_common import ConfigTester
  from .test_generation_utils import GenerationTesterMixin
  from .test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
  
  
  if is_torch_available():
      import torch
  
      from transformers import (
          XLMForMultipleChoice,
          XLMForQuestionAnswering,
          XLMForQuestionAnsweringSimple,
          XLMForSequenceClassification,
          XLMForTokenClassification,
          XLMModel,
          XLMWithLMHeadModel,
      )
      from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  class XLMModelTester:
      def __init__(
          self,
          parent,
      ):
          self.parent = parent
          self.batch_size = 13
          self.seq_length = 7
          self.is_training = True
          self.use_input_lengths = True
          self.use_token_type_ids = True
          self.use_labels = True
          self.gelu_activation = True
          self.sinusoidal_embeddings = False
          self.causal = False
          self.asm = False
          self.n_langs = 2
          self.vocab_size = 99
          self.n_special = 0
          self.hidden_size = 32
          self.num_hidden_layers = 5
          self.num_attention_heads = 4
          self.hidden_dropout_prob = 0.1
          self.attention_probs_dropout_prob = 0.1
          self.max_position_embeddings = 512
          self.type_sequence_label_size = 2
          self.initializer_range = 0.02
          self.num_labels = 2
          self.num_choices = 4
          self.summary_type = "last"
          self.use_proj = True
          self.scope = None
          self.bos_token_id = 0
  
      def prepare_config_and_inputs(self):
          input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
          input_mask = random_attention_mask([self.batch_size, self.seq_length])
  
          input_lengths = None
          if self.use_input_lengths:
              input_lengths = (
                  ids_tensor([self.batch_size], vocab_size=2) + self.seq_length - 2
              )  # small variation of seq_length
  
          token_type_ids = None
          if self.use_token_type_ids:
              token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.n_langs)
  
          sequence_labels = None
          token_labels = None
          is_impossible_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)
              is_impossible_labels = ids_tensor([self.batch_size], 2).float()
              choice_labels = ids_tensor([self.batch_size], self.num_choices)
  
          config = self.get_config()
  
          return (
              config,
              input_ids,
              token_type_ids,
              input_lengths,
              sequence_labels,
              token_labels,
              is_impossible_labels,
              choice_labels,
              input_mask,
          )
  
      def get_config(self):
          return XLMConfig(
              vocab_size=self.vocab_size,
              n_special=self.n_special,
              emb_dim=self.hidden_size,
              n_layers=self.num_hidden_layers,
              n_heads=self.num_attention_heads,
              dropout=self.hidden_dropout_prob,
              attention_dropout=self.attention_probs_dropout_prob,
              gelu_activation=self.gelu_activation,
              sinusoidal_embeddings=self.sinusoidal_embeddings,
              asm=self.asm,
              causal=self.causal,
              n_langs=self.n_langs,
              max_position_embeddings=self.max_position_embeddings,
              initializer_range=self.initializer_range,
              summary_type=self.summary_type,
              use_proj=self.use_proj,
              num_labels=self.num_labels,
              bos_token_id=self.bos_token_id,
          )
  
      def create_and_check_xlm_model(
          self,
          config,
          input_ids,
          token_type_ids,
          input_lengths,
          sequence_labels,
          token_labels,
          is_impossible_labels,
          choice_labels,
          input_mask,
      ):
          model = XLMModel(config=config)
          model.to(torch_device)
          model.eval()
          result = model(input_ids, lengths=input_lengths, langs=token_type_ids)
          result = model(input_ids, langs=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_xlm_lm_head(
          self,
          config,
          input_ids,
          token_type_ids,
          input_lengths,
          sequence_labels,
          token_labels,
          is_impossible_labels,
          choice_labels,
          input_mask,
      ):
          model = XLMWithLMHeadModel(config)
          model.to(torch_device)
          model.eval()
  
          result = model(input_ids, token_type_ids=token_type_ids, labels=token_labels)
          self.parent.assertEqual(result.loss.shape, ())
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
  
      def create_and_check_xlm_simple_qa(
          self,
          config,
          input_ids,
          token_type_ids,
          input_lengths,
          sequence_labels,
          token_labels,
          is_impossible_labels,
          choice_labels,
          input_mask,
      ):
          model = XLMForQuestionAnsweringSimple(config)
          model.to(torch_device)
          model.eval()
  
          outputs = model(input_ids)
  
          outputs = model(input_ids, start_positions=sequence_labels, end_positions=sequence_labels)
          result = outputs
          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 create_and_check_xlm_qa(
          self,
          config,
          input_ids,
          token_type_ids,
          input_lengths,
          sequence_labels,
          token_labels,
          is_impossible_labels,
          choice_labels,
          input_mask,
      ):
          model = XLMForQuestionAnswering(config)
          model.to(torch_device)
          model.eval()
  
          result = model(input_ids)
  
          result_with_labels = model(
              input_ids,
              start_positions=sequence_labels,
              end_positions=sequence_labels,
              cls_index=sequence_labels,
              is_impossible=is_impossible_labels,
              p_mask=input_mask,
          )
  
          result_with_labels = model(
              input_ids,
              start_positions=sequence_labels,
              end_positions=sequence_labels,
              cls_index=sequence_labels,
              is_impossible=is_impossible_labels,
          )
  
          (total_loss,) = result_with_labels.to_tuple()
  
          result_with_labels = model(input_ids, start_positions=sequence_labels, end_positions=sequence_labels)
  
          (total_loss,) = result_with_labels.to_tuple()
  
          self.parent.assertEqual(result_with_labels.loss.shape, ())
          self.parent.assertEqual(result.start_top_log_probs.shape, (self.batch_size, model.config.start_n_top))
          self.parent.assertEqual(result.start_top_index.shape, (self.batch_size, model.config.start_n_top))
          self.parent.assertEqual(
              result.end_top_log_probs.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
          )
          self.parent.assertEqual(
              result.end_top_index.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
          )
          self.parent.assertEqual(result.cls_logits.shape, (self.batch_size,))
  
      def create_and_check_xlm_sequence_classif(
          self,
          config,
          input_ids,
          token_type_ids,
          input_lengths,
          sequence_labels,
          token_labels,
          is_impossible_labels,
          choice_labels,
          input_mask,
      ):
          model = XLMForSequenceClassification(config)
          model.to(torch_device)
          model.eval()
  
          result = model(input_ids)
          result = model(input_ids, labels=sequence_labels)
          self.parent.assertEqual(result.loss.shape, ())
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
  
      def create_and_check_xlm_token_classif(
          self,
          config,
          input_ids,
          token_type_ids,
          input_lengths,
          sequence_labels,
          token_labels,
          is_impossible_labels,
          choice_labels,
          input_mask,
      ):
          config.num_labels = self.num_labels
          model = XLMForTokenClassification(config)
          model.to(torch_device)
          model.eval()
  
          result = model(input_ids, attention_mask=input_mask, labels=token_labels)
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
  
      def create_and_check_xlm_for_multiple_choice(
          self,
          config,
          input_ids,
          token_type_ids,
          input_lengths,
          sequence_labels,
          token_labels,
          is_impossible_labels,
          choice_labels,
          input_mask,
      ):
          config.num_choices = self.num_choices
          model = XLMForMultipleChoice(config=config)
          model.to(torch_device)
          model.eval()
          multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
          multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
          multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
          result = model(
              multiple_choice_inputs_ids,
              attention_mask=multiple_choice_input_mask,
              token_type_ids=multiple_choice_token_type_ids,
              labels=choice_labels,
          )
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
  
      def prepare_config_and_inputs_for_common(self):
          config_and_inputs = self.prepare_config_and_inputs()
          (
              config,
              input_ids,
              token_type_ids,
              input_lengths,
              sequence_labels,
              token_labels,
              is_impossible_labels,
              choice_labels,
              input_mask,
          ) = config_and_inputs
          inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
          return config, inputs_dict
  
  
  @require_torch
  class XLMModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
  
      all_model_classes = (
          (
              XLMModel,
              XLMWithLMHeadModel,
              XLMForQuestionAnswering,
              XLMForSequenceClassification,
              XLMForQuestionAnsweringSimple,
              XLMForTokenClassification,
              XLMForMultipleChoice,
          )
          if is_torch_available()
          else ()
      )
      all_generative_model_classes = (
          (XLMWithLMHeadModel,) if is_torch_available() else ()
      )  # TODO (PVP): Check other models whether language generation is also applicable
      test_sequence_classification_problem_types = True
  
      # XLM has 2 QA models -> need to manually set the correct labels for one of them here
      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 return_labels:
              if model_class.__name__ == "XLMForQuestionAnswering":
                  inputs_dict["start_positions"] = torch.zeros(
                      self.model_tester.batch_size, dtype=torch.long, device=torch_device
                  )
                  inputs_dict["end_positions"] = torch.zeros(
                      self.model_tester.batch_size, dtype=torch.long, device=torch_device
                  )
  
          return inputs_dict
  
      def setUp(self):
          self.model_tester = XLMModelTester(self)
          self.config_tester = ConfigTester(self, config_class=XLMConfig, emb_dim=37)
  
      def test_config(self):
          self.config_tester.run_common_tests()
  
      def test_xlm_model(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_model(*config_and_inputs)
  
      def test_xlm_lm_head(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_lm_head(*config_and_inputs)
  
      def test_xlm_simple_qa(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_simple_qa(*config_and_inputs)
  
      def test_xlm_qa(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_qa(*config_and_inputs)
  
      def test_xlm_sequence_classif(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_sequence_classif(*config_and_inputs)
  
      def test_xlm_token_classif(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_token_classif(*config_and_inputs)
  
      def test_xlm_for_multiple_choice(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_for_multiple_choice(*config_and_inputs)
  
      def _check_attentions_for_generate(
          self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
      ):
          self.assertIsInstance(attentions, tuple)
          self.assertListEqual(
              [isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions)
          )
          self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups)
  
          for idx, iter_attentions in enumerate(attentions):
              # adds PAD dummy token
              tgt_len = min_length + idx + 1
              src_len = min_length + idx + 1
  
              expected_shape = (
                  batch_size * num_beam_groups,
                  config.num_attention_heads,
                  tgt_len,
                  src_len,
              )
              # check attn size
              self.assertListEqual(
                  [layer_attention.shape for layer_attention in iter_attentions], [expected_shape] * len(iter_attentions)
              )
  
      def _check_hidden_states_for_generate(
          self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1
      ):
          self.assertIsInstance(hidden_states, tuple)
          self.assertListEqual(
              [isinstance(iter_hidden_states, tuple) for iter_hidden_states in hidden_states],
              [True] * len(hidden_states),
          )
          self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups)
  
          for idx, iter_hidden_states in enumerate(hidden_states):
              # adds PAD dummy token
              seq_len = min_length + idx + 1
              expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size)
              # check hidden size
              self.assertListEqual(
                  [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states],
                  [expected_shape] * len(iter_hidden_states),
              )
          pass
  
      @slow
      def test_model_from_pretrained(self):
          for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              model = XLMModel.from_pretrained(model_name)
              self.assertIsNotNone(model)
  
  
  @require_torch
  class XLMModelLanguageGenerationTest(unittest.TestCase):
      @slow
      def test_lm_generate_xlm_mlm_en_2048(self):
          model = XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048")
          model.to(torch_device)
          input_ids = torch.tensor([[14, 447]], dtype=torch.long, device=torch_device)  # the president
          expected_output_ids = [
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
          ]  # the president the president the president the president the president the president the president the president the president the president
          # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
          output_ids = model.generate(input_ids, do_sample=False)
          self.assertListEqual(output_ids[0].cpu().numpy().tolist(), expected_output_ids)
  