






import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


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 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			List[str]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Union[str, Any]=13       ,				lowerCamelCase		:			Any=7       ,				lowerCamelCase		:			int=True       ,				lowerCamelCase		:			Dict=True       ,				lowerCamelCase		:			Optional[Any]=True       ,				lowerCamelCase		:			int=True       ,				lowerCamelCase		:			int=True       ,				lowerCamelCase		:			Tuple=False       ,				lowerCamelCase		:			Tuple=False       ,				lowerCamelCase		:			Optional[int]=False       ,				lowerCamelCase		:			List[str]=2       ,				lowerCamelCase		:			Any=99       ,				lowerCamelCase		:			Optional[int]=0       ,				lowerCamelCase		:			Dict=32       ,				lowerCamelCase		:			Optional[int]=5       ,				lowerCamelCase		:			Union[str, Any]=4       ,				lowerCamelCase		:			str=0.1       ,				lowerCamelCase		:			Any=0.1       ,				lowerCamelCase		:			Dict=512       ,				lowerCamelCase		:			Dict=2       ,				lowerCamelCase		:			Any=0.02       ,				lowerCamelCase		:			int=2       ,				lowerCamelCase		:			Optional[int]=4       ,				lowerCamelCase		:			Optional[int]="last"       ,				lowerCamelCase		:			List[Any]=True       ,				lowerCamelCase		:			List[str]=None       ,				lowerCamelCase		:			Tuple=0       ,				)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    seq_length
         _UpperCAmelCase							=    is_training
         _UpperCAmelCase							=    use_input_lengths
         _UpperCAmelCase							=    use_token_type_ids
         _UpperCAmelCase							=    use_labels
         _UpperCAmelCase							=    gelu_activation
         _UpperCAmelCase							=    sinusoidal_embeddings
         _UpperCAmelCase							=    causal
         _UpperCAmelCase							=    asm
         _UpperCAmelCase							=    n_langs
         _UpperCAmelCase							=    vocab_size
         _UpperCAmelCase							=    n_special
         _UpperCAmelCase							=    hidden_size
         _UpperCAmelCase							=    num_hidden_layers
         _UpperCAmelCase							=    num_attention_heads
         _UpperCAmelCase							=    hidden_dropout_prob
         _UpperCAmelCase							=    attention_probs_dropout_prob
         _UpperCAmelCase							=    max_position_embeddings
         _UpperCAmelCase							=    type_sequence_label_size
         _UpperCAmelCase							=    initializer_range
         _UpperCAmelCase							=    num_labels
         _UpperCAmelCase							=    num_choices
         _UpperCAmelCase							=    summary_type
         _UpperCAmelCase							=    use_proj
         _UpperCAmelCase							=    scope
         _UpperCAmelCase							=    bos_token_id


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size							)
         _UpperCAmelCase							=    random_attention_mask([self.batch_size, self.seq_length]							)

         _UpperCAmelCase							=    None
         if self.use_input_lengths:
                _UpperCAmelCase							=    (
                    ids_tensor([self.batch_size]       ,				vocab_size=2							) + self.seq_length - 2
                )  # small variation of seq_length

         _UpperCAmelCase							=    None
         if self.use_token_type_ids:
                _UpperCAmelCase							=    ids_tensor([self.batch_size, self.seq_length]       ,				self.n_langs							)

         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    None
         if self.use_labels:
                _UpperCAmelCase							=    ids_tensor([self.batch_size]       ,				self.type_sequence_label_size							)
                _UpperCAmelCase							=    ids_tensor([self.batch_size, self.seq_length]       ,				self.num_labels							)
                _UpperCAmelCase							=    ids_tensor([self.batch_size]       ,				2							).float()
                _UpperCAmelCase							=    ids_tensor([self.batch_size]       ,				self.num_choices							)

         _UpperCAmelCase							=    self.get_config()

         return (
             config,
             input_ids,
             token_type_ids,
             input_lengths,
             sequence_labels,
             token_labels,
             is_impossible_labels,
             choice_labels,
             input_mask,
         )


  def  lowerCamelCase       (      self		:			List[str]							)						->	Union[str, Any]:


         """simple docstring"""
         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  lowerCamelCase       (      self		:			str       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			str       ,				)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    XLMModel(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase       ,				lengths=lowerCamelCase       ,				langs=lowerCamelCase							)
         _UpperCAmelCase							=    model(lowerCamelCase       ,				langs=lowerCamelCase							)
         _UpperCAmelCase							=    model(lowerCamelCase							)
         self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)							)


  def  lowerCamelCase       (      self		:			int       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			Optional[Any]       ,				)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    XLMWithLMHeadModel(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()

         _UpperCAmelCase							=    model(lowerCamelCase       ,				token_type_ids=lowerCamelCase       ,				labels=lowerCamelCase							)
         self.parent.assertEqual(result.loss.shape       ,				()							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.seq_length, self.vocab_size)							)


  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Optional[Any]       ,				)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    XLMForQuestionAnsweringSimple(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()

         _UpperCAmelCase							=    model(lowerCamelCase							)

         _UpperCAmelCase							=    model(lowerCamelCase       ,				start_positions=lowerCamelCase       ,				end_positions=lowerCamelCase							)
         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			str       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			Dict       ,				)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    XLMForQuestionAnswering(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()

         _UpperCAmelCase							=    model(lowerCamelCase							)

         _UpperCAmelCase							=    model(
             lowerCamelCase       ,				start_positions=lowerCamelCase       ,				end_positions=lowerCamelCase       ,				cls_index=lowerCamelCase       ,				is_impossible=lowerCamelCase       ,				p_mask=lowerCamelCase       ,				)

         _UpperCAmelCase							=    model(
             lowerCamelCase       ,				start_positions=lowerCamelCase       ,				end_positions=lowerCamelCase       ,				cls_index=lowerCamelCase       ,				is_impossible=lowerCamelCase       ,				)

         ((_UpperCAmelCase)	,			)							=    result_with_labels.to_tuple()

         _UpperCAmelCase							=    model(lowerCamelCase       ,				start_positions=lowerCamelCase       ,				end_positions=lowerCamelCase							)

         ((_UpperCAmelCase)	,			)							=    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  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			List[Any]       ,				)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    XLMForSequenceClassification(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()

         _UpperCAmelCase							=    model(lowerCamelCase							)
         _UpperCAmelCase							=    model(lowerCamelCase       ,				labels=lowerCamelCase							)
         self.parent.assertEqual(result.loss.shape       ,				()							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.type_sequence_label_size)							)


  def  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			List[Any]       ,				)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    self.num_labels
         _UpperCAmelCase							=    XLMForTokenClassification(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()

         _UpperCAmelCase							=    model(lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				labels=lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.seq_length, self.num_labels)							)


  def  lowerCamelCase       (      self		:			int       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			List[str]       ,				)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    self.num_choices
         _UpperCAmelCase							=    XLMForMultipleChoice(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    input_ids.unsqueeze(1							).expand(-1       ,				self.num_choices       ,				-1							).contiguous()
         _UpperCAmelCase							=    token_type_ids.unsqueeze(1							).expand(-1       ,				self.num_choices       ,				-1							).contiguous()
         _UpperCAmelCase							=    input_mask.unsqueeze(1							).expand(-1       ,				self.num_choices       ,				-1							).contiguous()
         _UpperCAmelCase							=    model(
             lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				token_type_ids=lowerCamelCase       ,				labels=lowerCamelCase       ,				)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_choices)							)





  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    self.prepare_config_and_inputs()
         (
             (
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			
         )							=    config_and_inputs
         _UpperCAmelCase							=    {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
         return config, inputs_dict



@require_torch
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      UpperCAmelCase					,      UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  (
      (
          XLMModel,
          XLMWithLMHeadModel,
          XLMForQuestionAnswering,
          XLMForSequenceClassification,
          XLMForQuestionAnsweringSimple,
          XLMForTokenClassification,
          XLMForMultipleChoice,
      )
      if is_torch_available()
      else ()
  )
  _lowerCamelCase    =  (
      (XLMWithLMHeadModel,) if is_torch_available() else ()
  )  # TODO (PVP): Check other models whether language generation is also applicable
  _lowerCamelCase    =  (
      {
          '''feature-extraction''': XLMModel,
          '''fill-mask''': XLMWithLMHeadModel,
          '''question-answering''': XLMForQuestionAnsweringSimple,
          '''text-classification''': XLMForSequenceClassification,
          '''text-generation''': XLMWithLMHeadModel,
          '''token-classification''': XLMForTokenClassification,
          '''zero-shot''': XLMForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Tuple							)						->	List[Any]:


         """simple docstring"""
         if (
             pipeline_test_casse_name == "QAPipelineTests"
             and tokenizer_name is not None
             and not tokenizer_name.endswith("""Fast"""							)
         ):
                # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
                # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
                # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
                return True

         return False


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Union[str, Any]=False							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    super()._prepare_for_class(lowerCamelCase       ,				lowerCamelCase       ,				return_labels=lowerCamelCase							)

         if return_labels:
                if model_class.__name__ == "XLMForQuestionAnswering":
                       _UpperCAmelCase							=    torch.zeros(
                           self.model_tester.batch_size       ,				dtype=torch.long       ,				device=lowerCamelCase							)
                       _UpperCAmelCase							=    torch.zeros(
                           self.model_tester.batch_size       ,				dtype=torch.long       ,				device=lowerCamelCase							)

         return inputs_dict


  def  lowerCamelCase       (      self		:			Dict							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    XLMModelTester(self							)
         _UpperCAmelCase							=    ConfigTester(self       ,				config_class=lowerCamelCase       ,				emb_dim=37							)


  def  lowerCamelCase       (      self		:			str							)						->	int:


         """simple docstring"""
         self.config_tester.run_common_tests()


  def  lowerCamelCase       (      self		:			str							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_model(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			int							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_lm_head(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Tuple							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_simple_qa(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Tuple							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_qa(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			int							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_sequence_classif(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_token_classif(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			List[str]							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_for_multiple_choice(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Any=False       ,				lowerCamelCase		:			List[Any]=1							)						->	Optional[int]:


         """simple docstring"""
         self.assertIsInstance(lowerCamelCase       ,				lowerCamelCase							)
         self.assertListEqual(
             [isinstance(lowerCamelCase       ,				lowerCamelCase							) for iter_attentions in attentions]       ,				[True] * len(lowerCamelCase							)							)
         self.assertEqual(len(lowerCamelCase							)       ,				(max_length - min_length) * num_beam_groups							)

         for idx, iter_attentions in enumerate(lowerCamelCase							):
                # adds PAD dummy token
                _UpperCAmelCase							=    min_length + idx + 1
                _UpperCAmelCase							=    min_length + idx + 1

                _UpperCAmelCase							=    (
                    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(lowerCamelCase							)							)


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Any=False       ,				lowerCamelCase		:			Union[str, Any]=1							)						->	str:


         """simple docstring"""
         self.assertIsInstance(lowerCamelCase       ,				lowerCamelCase							)
         self.assertListEqual(
             [isinstance(lowerCamelCase       ,				lowerCamelCase							) for iter_hidden_states in hidden_states]       ,				[True] * len(lowerCamelCase							)       ,				)
         self.assertEqual(len(lowerCamelCase							)       ,				(max_length - min_length) * num_beam_groups							)

         for idx, iter_hidden_states in enumerate(lowerCamelCase							):
                # adds PAD dummy token
                _UpperCAmelCase							=    min_length + idx + 1
                _UpperCAmelCase							=    (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(lowerCamelCase							)       ,				)
         pass





  @slow
  def  lowerCamelCase       (      self		:			int							)						->	Optional[Any]:


         """simple docstring"""
         for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase							=    XLMModel.from_pretrained(lowerCamelCase							)
                self.assertIsNotNone(lowerCamelCase							)



@require_torch
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  @slow
  def  lowerCamelCase       (      self		:			List[Any]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    XLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048"""							)
         model.to(lowerCamelCase							)
         _UpperCAmelCase							=    torch.tensor([[14, 447]]       ,				dtype=torch.long       ,				device=lowerCamelCase							)  # the president
         _UpperCAmelCase							=    [
             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
         _UpperCAmelCase							=    model.generate(lowerCamelCase       ,				do_sample=lowerCamelCase							)
         self.assertListEqual(output_ids[0].cpu().numpy().tolist()       ,				lowerCamelCase							)







import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      DebertaVaForMaskedLM,
      DebertaVaForMultipleChoice,
      DebertaVaForQuestionAnswering,
      DebertaVaForSequenceClassification,
      DebertaVaForTokenClassification,
      DebertaVaModel,
  )
  from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  def __init__(      self		:			Union[str, Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Optional[Any]=13       ,				lowerCamelCase		:			List[Any]=7       ,				lowerCamelCase		:			List[Any]=True       ,				lowerCamelCase		:			str=True       ,				lowerCamelCase		:			List[str]=True       ,				lowerCamelCase		:			List[str]=True       ,				lowerCamelCase		:			Optional[int]=99       ,				lowerCamelCase		:			int=32       ,				lowerCamelCase		:			str=5       ,				lowerCamelCase		:			str=4       ,				lowerCamelCase		:			List[str]=37       ,				lowerCamelCase		:			str="gelu"       ,				lowerCamelCase		:			Dict=0.1       ,				lowerCamelCase		:			Any=0.1       ,				lowerCamelCase		:			Union[str, Any]=512       ,				lowerCamelCase		:			Optional[int]=16       ,				lowerCamelCase		:			Any=2       ,				lowerCamelCase		:			Any=0.02       ,				lowerCamelCase		:			List[Any]=False       ,				lowerCamelCase		:			Optional[Any]=True       ,				lowerCamelCase		:			Optional[int]="None"       ,				lowerCamelCase		:			Tuple=3       ,				lowerCamelCase		:			int=4       ,				lowerCamelCase		:			Optional[Any]=None       ,				)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    seq_length
         _UpperCAmelCase							=    is_training
         _UpperCAmelCase							=    use_input_mask
         _UpperCAmelCase							=    use_token_type_ids
         _UpperCAmelCase							=    use_labels
         _UpperCAmelCase							=    vocab_size
         _UpperCAmelCase							=    hidden_size
         _UpperCAmelCase							=    num_hidden_layers
         _UpperCAmelCase							=    num_attention_heads
         _UpperCAmelCase							=    intermediate_size
         _UpperCAmelCase							=    hidden_act
         _UpperCAmelCase							=    hidden_dropout_prob
         _UpperCAmelCase							=    attention_probs_dropout_prob
         _UpperCAmelCase							=    max_position_embeddings
         _UpperCAmelCase							=    type_vocab_size
         _UpperCAmelCase							=    type_sequence_label_size
         _UpperCAmelCase							=    initializer_range
         _UpperCAmelCase							=    num_labels
         _UpperCAmelCase							=    num_choices
         _UpperCAmelCase							=    relative_attention
         _UpperCAmelCase							=    position_biased_input
         _UpperCAmelCase							=    pos_att_type
         _UpperCAmelCase							=    scope


  def  lowerCamelCase       (      self		:			str							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size							)

         _UpperCAmelCase							=    None
         if self.use_input_mask:
                _UpperCAmelCase							=    ids_tensor([self.batch_size, self.seq_length]       ,				vocab_size=2							)

         _UpperCAmelCase							=    None
         if self.use_token_type_ids:
                _UpperCAmelCase							=    ids_tensor([self.batch_size, self.seq_length]       ,				self.type_vocab_size							)

         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    None
         if self.use_labels:
                _UpperCAmelCase							=    ids_tensor([self.batch_size]       ,				self.type_sequence_label_size							)
                _UpperCAmelCase							=    ids_tensor([self.batch_size, self.seq_length]       ,				self.num_labels							)
                _UpperCAmelCase							=    ids_tensor([self.batch_size]       ,				self.num_choices							)

         _UpperCAmelCase							=    self.get_config()

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


  def  lowerCamelCase       (      self		:			Dict							)						->	Dict:


         """simple docstring"""
         return DebertaVaConfig(
             vocab_size=self.vocab_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       ,				initializer_range=self.initializer_range       ,				relative_attention=self.relative_attention       ,				position_biased_input=self.position_biased_input       ,				pos_att_type=self.pos_att_type       ,				)


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			List[Any]							)						->	Union[str, Any]:


         """simple docstring"""
         self.parent.assertListEqual(list(result.loss.size()							)       ,				[]							)


  def  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Any							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    DebertaVaModel(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				token_type_ids=lowerCamelCase							)[0]
         _UpperCAmelCase							=    model(lowerCamelCase       ,				token_type_ids=lowerCamelCase							)[0]
         _UpperCAmelCase							=    model(lowerCamelCase							)[0]

         self.parent.assertListEqual(list(sequence_output.size()							)       ,				[self.batch_size, self.seq_length, self.hidden_size]							)


  def  lowerCamelCase       (      self		:			List[str]       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			List[Any]							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    DebertaVaForMaskedLM(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				token_type_ids=lowerCamelCase       ,				labels=lowerCamelCase							)

         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.seq_length, self.vocab_size)							)


  def  lowerCamelCase       (      self		:			str       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Any							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.num_labels
         _UpperCAmelCase							=    DebertaVaForSequenceClassification(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				token_type_ids=lowerCamelCase       ,				labels=lowerCamelCase							)
         self.parent.assertListEqual(list(result.logits.size()							)       ,				[self.batch_size, self.num_labels]							)
         self.check_loss_output(lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Union[str, Any]							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    self.num_labels
         _UpperCAmelCase							=    DebertaVaForTokenClassification(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				token_type_ids=lowerCamelCase       ,				labels=lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.seq_length, self.num_labels)							)


  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			str							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    DebertaVaForQuestionAnswering(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(
             lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				token_type_ids=lowerCamelCase       ,				start_positions=lowerCamelCase       ,				end_positions=lowerCamelCase       ,				)
         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  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			str							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    DebertaVaForMultipleChoice(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    input_ids.unsqueeze(1							).expand(-1       ,				self.num_choices       ,				-1							).contiguous()
         _UpperCAmelCase							=    token_type_ids.unsqueeze(1							).expand(-1       ,				self.num_choices       ,				-1							).contiguous()
         _UpperCAmelCase							=    input_mask.unsqueeze(1							).expand(-1       ,				self.num_choices       ,				-1							).contiguous()
         _UpperCAmelCase							=    model(
             lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				token_type_ids=lowerCamelCase       ,				labels=lowerCamelCase       ,				)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_choices)							)





  def  lowerCamelCase       (      self		:			List[str]							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    self.prepare_config_and_inputs()
         (
             (
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			
         )							=    config_and_inputs
         _UpperCAmelCase							=    {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
         return config, inputs_dict



@require_torch
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  (
      (
          DebertaVaModel,
          DebertaVaForMaskedLM,
          DebertaVaForSequenceClassification,
          DebertaVaForTokenClassification,
          DebertaVaForQuestionAnswering,
          DebertaVaForMultipleChoice,
      )
      if is_torch_available()
      else ()
  )
  _lowerCamelCase    =  (
      {
          '''feature-extraction''': DebertaVaModel,
          '''fill-mask''': DebertaVaForMaskedLM,
          '''question-answering''': DebertaVaForQuestionAnswering,
          '''text-classification''': DebertaVaForSequenceClassification,
          '''token-classification''': DebertaVaForTokenClassification,
          '''zero-shot''': DebertaVaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )

  _lowerCamelCase    =  True
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False


  def  lowerCamelCase       (      self		:			int							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    DebertaVaModelTester(self							)
         _UpperCAmelCase							=    ConfigTester(self       ,				config_class=lowerCamelCase       ,				hidden_size=37							)


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Tuple:


         """simple docstring"""
         self.config_tester.run_common_tests()


  def  lowerCamelCase       (      self		:			Tuple							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_model(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Tuple							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_question_answering(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Dict							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_token_classification(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			int							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_multiple_choice(*lowerCamelCase							)





  @slow
  def  lowerCamelCase       (      self		:			Dict							)						->	Optional[int]:


         """simple docstring"""
         for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase							=    DebertaVaModel.from_pretrained(lowerCamelCase							)
                self.assertIsNotNone(lowerCamelCase							)



@require_torch
@require_sentencepiece
@require_tokenizers
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  @unittest.skip(reason="""Model not available yet"""							)
  def  lowerCamelCase       (      self		:			Any							)						->	Tuple:


         """simple docstring"""
         pass





  @slow
  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    DebertaVaModel.from_pretrained("""microsoft/deberta-v2-xlarge"""							)

         _UpperCAmelCase							=    torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]							)
         _UpperCAmelCase							=    torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
         with torch.no_grad():
                _UpperCAmelCase							=    model(lowerCamelCase       ,				attention_mask=lowerCamelCase							)[0]
         # compare the actual values for a slice.
         _UpperCAmelCase							=    torch.tensor(
             [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]							)
         self.assertTrue(torch.allclose(output[:, 1:4, 1:4]       ,				lowerCamelCase       ,				atol=1E-4							)       ,				f"""{output[:, 1:4, 1:4]}"""							)