code: # 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.
  
  
  import unittest
  
  from transformers import is_torch_available
  
  from .test_configuration_common import ConfigTester
  from .test_modeling_common import ModelTesterMixin, ids_tensor
  from .utils import CACHE_DIR, require_torch, slow, torch_device
  
  
  if is_torch_available():
      import torch
      from transformers import (
          XLMConfig,
          XLMModel,
          XLMWithLMHeadModel,
          XLMForTokenClassification,
          XLMForQuestionAnswering,
          XLMForSequenceClassification,
          XLMForQuestionAnsweringSimple,
      )
      from transformers.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_MAP
  
  
  @require_torch
  class XLMModelTest(ModelTesterMixin, unittest.TestCase):
  
      all_model_classes = (
          (
              XLMModel,
              XLMWithLMHeadModel,
              XLMForQuestionAnswering,
              XLMForSequenceClassification,
              XLMForQuestionAnsweringSimple,
          )
          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
  
      class XLMModelTester(object):
          def __init__(
              self,
              parent,
              batch_size=13,
              seq_length=7,
              is_training=True,
              use_input_lengths=True,
              use_token_type_ids=True,
              use_labels=True,
              gelu_activation=True,
              sinusoidal_embeddings=False,
              causal=False,
              asm=False,
              n_langs=2,
              vocab_size=99,
              n_special=0,
              hidden_size=32,
              num_hidden_layers=5,
              num_attention_heads=4,
              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,
              summary_type="last",
              use_proj=True,
              scope=None,
              bos_token_id=0,
          ):
              self.parent = parent
              self.batch_size = batch_size
              self.seq_length = seq_length
              self.is_training = is_training
              self.use_input_lengths = use_input_lengths
              self.use_token_type_ids = use_token_type_ids
              self.use_labels = use_labels
              self.gelu_activation = gelu_activation
              self.sinusoidal_embeddings = sinusoidal_embeddings
              self.asm = asm
              self.n_langs = n_langs
              self.vocab_size = vocab_size
              self.n_special = n_special
              self.summary_type = summary_type
              self.causal = causal
              self.use_proj = use_proj
              self.hidden_size = hidden_size
              self.num_hidden_layers = num_hidden_layers
              self.num_attention_heads = num_attention_heads
              self.hidden_dropout_prob = hidden_dropout_prob
              self.attention_probs_dropout_prob = attention_probs_dropout_prob
              self.max_position_embeddings = max_position_embeddings
              self.n_langs = n_langs
              self.type_sequence_label_size = type_sequence_label_size
              self.initializer_range = initializer_range
              self.summary_type = summary_type
              self.num_labels = num_labels
              self.num_choices = num_choices
              self.scope = scope
              self.bos_token_id = bos_token_id
  
          def prepare_config_and_inputs(self):
              input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
              input_mask = ids_tensor([self.batch_size, self.seq_length], 2).float()
  
              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()
  
              config = 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,
                  bos_token_id=self.bos_token_id,
              )
  
              return (
                  config,
                  input_ids,
                  token_type_ids,
                  input_lengths,
                  sequence_labels,
                  token_labels,
                  is_impossible_labels,
                  input_mask,
              )
  
          def check_loss_output(self, result):
              self.parent.assertListEqual(list(result["loss"].size()), [])
  
          def create_and_check_xlm_model(
              self,
              config,
              input_ids,
              token_type_ids,
              input_lengths,
              sequence_labels,
              token_labels,
              is_impossible_labels,
              input_mask,
          ):
              model = XLMModel(config=config)
              model.to(torch_device)
              model.eval()
              outputs = model(input_ids, lengths=input_lengths, langs=token_type_ids)
              outputs = model(input_ids, langs=token_type_ids)
              outputs = model(input_ids)
              sequence_output = outputs[0]
              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_xlm_lm_head(
              self,
              config,
              input_ids,
              token_type_ids,
              input_lengths,
              sequence_labels,
              token_labels,
              is_impossible_labels,
              input_mask,
          ):
              model = XLMWithLMHeadModel(config)
              model.to(torch_device)
              model.eval()
  
              loss, logits = model(input_ids, token_type_ids=token_type_ids, labels=token_labels)
  
              result = {
                  "loss": loss,
                  "logits": logits,
              }
  
              self.parent.assertListEqual(list(result["loss"].size()), [])
              self.parent.assertListEqual(
                  list(result["logits"].size()), [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,
              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)
              loss, start_logits, end_logits = outputs
  
              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_xlm_qa(
              self,
              config,
              input_ids,
              token_type_ids,
              input_lengths,
              sequence_labels,
              token_labels,
              is_impossible_labels,
              input_mask,
          ):
              model = XLMForQuestionAnswering(config)
              model.to(torch_device)
              model.eval()
  
              outputs = model(input_ids)
              start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits = outputs
  
              outputs = model(
                  input_ids,
                  start_positions=sequence_labels,
                  end_positions=sequence_labels,
                  cls_index=sequence_labels,
                  is_impossible=is_impossible_labels,
                  p_mask=input_mask,
              )
  
              outputs = model(
                  input_ids,
                  start_positions=sequence_labels,
                  end_positions=sequence_labels,
                  cls_index=sequence_labels,
                  is_impossible=is_impossible_labels,
              )
  
              (total_loss,) = outputs
  
              outputs = model(input_ids, start_positions=sequence_labels, end_positions=sequence_labels)
  
              (total_loss,) = outputs
  
              result = {
                  "loss": total_loss,
                  "start_top_log_probs": start_top_log_probs,
                  "start_top_index": start_top_index,
                  "end_top_log_probs": end_top_log_probs,
                  "end_top_index": end_top_index,
                  "cls_logits": cls_logits,
              }
  
              self.parent.assertListEqual(list(result["loss"].size()), [])
              self.parent.assertListEqual(
                  list(result["start_top_log_probs"].size()), [self.batch_size, model.config.start_n_top]
              )
              self.parent.assertListEqual(
                  list(result["start_top_index"].size()), [self.batch_size, model.config.start_n_top]
              )
              self.parent.assertListEqual(
                  list(result["end_top_log_probs"].size()),
                  [self.batch_size, model.config.start_n_top * model.config.end_n_top],
              )
              self.parent.assertListEqual(
                  list(result["end_top_index"].size()),
                  [self.batch_size, model.config.start_n_top * model.config.end_n_top],
              )
              self.parent.assertListEqual(list(result["cls_logits"].size()), [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,
              input_mask,
          ):
              model = XLMForSequenceClassification(config)
              model.to(torch_device)
              model.eval()
  
              (logits,) = model(input_ids)
              loss, logits = model(input_ids, labels=sequence_labels)
  
              result = {
                  "loss": loss,
                  "logits": logits,
              }
  
              self.parent.assertListEqual(list(result["loss"].size()), [])
              self.parent.assertListEqual(
                  list(result["logits"].size()), [self.batch_size, self.type_sequence_label_size]
              )
  
          def create_and_check_xlm_for_token_classification(
              self,
              config,
              input_ids,
              token_type_ids,
              input_lengths,
              sequence_labels,
              token_labels,
              is_impossible_labels,
              input_mask,
          ):
              config.num_labels = self.num_labels
              model = XLMForTokenClassification(config)
              model.to(torch_device)
              model.eval()
  
              loss, logits = model(input_ids, attention_mask=input_mask, labels=token_labels)
              result = {
                  "loss": loss,
                  "logits": logits,
              }
              self.parent.assertListEqual(
                  list(result["logits"].size()), [self.batch_size, self.seq_length, 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,
                  token_type_ids,
                  input_lengths,
                  sequence_labels,
                  token_labels,
                  is_impossible_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
  
      def setUp(self):
          self.model_tester = XLMModelTest.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_for_token_classification(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_for_token_classification(*config_and_inputs)
  
      @slow
      def test_model_from_pretrained(self):
          for model_name in list(XLM_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
              model = XLMModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
              self.assertIsNotNone(model)
  
  
  class XLMModelLanguageGenerationTest(unittest.TestCase):
      @slow
      def test_lm_generate_xlm_mlm_en_2048(self):
          model = XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048")
          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].numpy().tolist(), expected_output_ids)
  