






"""simple docstring"""





import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A__					: Any=	"""▁"""

A__					: List[str]=	get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
class    __lowerCamelCase    (  _a							,	unittest.TestCase  ):
			a      :				List[Any] 						=BertGenerationTokenizer
			a      :				Union[str, Any] 						=False
			a      :				List[str] 						=True

			def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
							super().setUp()

							UpperCamelCase__         =    BertGenerationTokenizer(snake_case_ ,				keep_accents=snake_case_      )
							tokenizer.save_pretrained(self.tmpdirname      )

			def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
							UpperCamelCase__         =    '<s>'
							UpperCamelCase__         =    1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_      ) ,				snake_case_      )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_      ) ,				snake_case_      )

			def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
							UpperCamelCase__         =    list(self.get_tokenizer().get_vocab().keys()      )

							self.assertEqual(vocab_keys[0] ,				'<unk>'      )
							self.assertEqual(vocab_keys[1] ,				'<s>'      )
							self.assertEqual(vocab_keys[-1] ,				'<pad>'      )
							self.assertEqual(len(snake_case_      ) ,				1002      )

			def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
							self.assertEqual(self.get_tokenizer().vocab_size ,				1000      )

			def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
							UpperCamelCase__         =    BertGenerationTokenizer(snake_case_ ,				keep_accents=snake_case_      )

							UpperCamelCase__         =    tokenizer.tokenize('This is a test'      )
							self.assertListEqual(snake_case_ ,				['▁This', '▁is', '▁a', '▁t', 'est']      )

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(snake_case_      ) ,				[285, 46, 10, 170, 382] ,				)

							UpperCamelCase__         =    tokenizer.tokenize('I was born in 92000, and this is falsé.'      )
							self.assertListEqual(
							    snake_case_ ,				[
							        SPIECE_UNDERLINE + 'I',
							        SPIECE_UNDERLINE + 'was',
							        SPIECE_UNDERLINE + 'b',
							        'or',
							        'n',
							        SPIECE_UNDERLINE + 'in',
							        SPIECE_UNDERLINE + '',
							        '9',
							        '2',
							        '0',
							        '0',
							        '0',
							        ',',
							        SPIECE_UNDERLINE + 'and',
							        SPIECE_UNDERLINE + 'this',
							        SPIECE_UNDERLINE + 'is',
							        SPIECE_UNDERLINE + 'f',
							        'al',
							        's',
							        'é',
							        '.',
							    ] ,				)
							UpperCamelCase__         =    tokenizer.convert_tokens_to_ids(snake_case_      )
							self.assertListEqual(
							    snake_case_ ,				[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] ,				)

							UpperCamelCase__         =    tokenizer.convert_ids_to_tokens(snake_case_      )
							self.assertListEqual(
							    snake_case_ ,				[
							        SPIECE_UNDERLINE + 'I',
							        SPIECE_UNDERLINE + 'was',
							        SPIECE_UNDERLINE + 'b',
							        'or',
							        'n',
							        SPIECE_UNDERLINE + 'in',
							        SPIECE_UNDERLINE + '',
							        '<unk>',
							        '2',
							        '0',
							        '0',
							        '0',
							        ',',
							        SPIECE_UNDERLINE + 'and',
							        SPIECE_UNDERLINE + 'this',
							        SPIECE_UNDERLINE + 'is',
							        SPIECE_UNDERLINE + 'f',
							        'al',
							        's',
							        '<unk>',
							        '.',
							    ] ,				)

			@cached_property
			def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
							return BertGenerationTokenizer.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'      )

			@slow
			def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
							UpperCamelCase__         =    'Hello World!'
							UpperCamelCase__         =    [1_8536, 2260, 101]

							self.assertListEqual(snake_case_ ,				self.big_tokenizer.encode(snake_case_      )      )

			@slow
			def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
							UpperCamelCase__         =    (
							    'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
							    ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
							)
							UpperCamelCase__         =    [
							    871,
							    419,
							    358,
							    946,
							    991,
							    2521,
							    452,
							    358,
							    1357,
							    387,
							    7751,
							    3536,
							    112,
							    985,
							    456,
							    126,
							    865,
							    938,
							    5400,
							    5734,
							    458,
							    1368,
							    467,
							    786,
							    2462,
							    5246,
							    1159,
							    633,
							    865,
							    4519,
							    457,
							    582,
							    852,
							    2557,
							    427,
							    916,
							    508,
							    405,
							    3_4324,
							    497,
							    391,
							    408,
							    1_1342,
							    1244,
							    385,
							    100,
							    938,
							    985,
							    456,
							    574,
							    362,
							    1_2597,
							    3200,
							    3129,
							    1172,
							]

							self.assertListEqual(snake_case_ ,				self.big_tokenizer.encode(snake_case_      )      )

			@require_torch
			@slow
			def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
							import torch

							from transformers import BertGenerationConfig, BertGenerationEncoder

							# Build sequence
							UpperCamelCase__         =    list(self.big_tokenizer.get_vocab().keys()      )[:10]
							UpperCamelCase__         =    ' '.join(snake_case_      )
							UpperCamelCase__         =    self.big_tokenizer.encode_plus(snake_case_ ,				return_tensors='pt' ,				return_token_type_ids=snake_case_      )
							UpperCamelCase__         =    self.big_tokenizer.batch_encode_plus(
							    [sequence + ' ' + sequence] ,				return_tensors='pt' ,				return_token_type_ids=snake_case_      )

							UpperCamelCase__         =    BertGenerationConfig()
							UpperCamelCase__         =    BertGenerationEncoder(snake_case_      )

							assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

							with torch.no_grad():
											model(**snake_case_      )
											model(**snake_case_      )







			@slow
			def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
							# fmt: off
							UpperCamelCase__         =    {'input_ids': [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=snake_case_ ,				model_name='google/bert_for_seq_generation_L-24_bbc_encoder' ,				revision='c817d1fd1be2ffa69431227a1fe320544943d4db' ,				)












"""simple docstring"""





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    __lowerCamelCase    (  _a  ):

   def __init__(					self ,				snake_case_ ,				snake_case_=13 ,				snake_case_=7 ,				snake_case_=True ,				snake_case_=True ,				snake_case_=True ,				snake_case_=True ,				snake_case_=99 ,				snake_case_=32 ,				snake_case_=5 ,				snake_case_=4 ,				snake_case_=37 ,				snake_case_="gelu" ,				snake_case_=0.1 ,				snake_case_=0.1 ,				snake_case_=512 ,				snake_case_=16 ,				snake_case_=2 ,				snake_case_=0.02 ,				snake_case_=False ,				snake_case_=True ,				snake_case_="None" ,				snake_case_=3 ,				snake_case_=4 ,				snake_case_=None ,				)		->       str:
       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 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       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 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       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 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Any:
       self.parent.assertListEqual(list(result.loss.size()      ) ,				[]      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       str:
       UpperCamelCase__         =    DebertaVaModel(config=snake_case_      )
       model.to(snake_case_      )
       model.eval()
       UpperCamelCase__         =    model(snake_case_ ,				attention_mask=snake_case_ ,				token_type_ids=snake_case_      )[0]
       UpperCamelCase__         =    model(snake_case_ ,				token_type_ids=snake_case_      )[0]
       UpperCamelCase__         =    model(snake_case_      )[0]

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

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       Optional[Any]:
       UpperCamelCase__         =    DebertaVaForMaskedLM(config=snake_case_      )
       model.to(snake_case_      )
       model.eval()
       UpperCamelCase__         =    model(snake_case_ ,				attention_mask=snake_case_ ,				token_type_ids=snake_case_ ,				labels=snake_case_      )

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

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       Dict:
       UpperCamelCase__         =    self.num_labels
       UpperCamelCase__         =    DebertaVaForSequenceClassification(snake_case_      )
       model.to(snake_case_      )
       model.eval()
       UpperCamelCase__         =    model(snake_case_ ,				attention_mask=snake_case_ ,				token_type_ids=snake_case_ ,				labels=snake_case_      )
       self.parent.assertListEqual(list(result.logits.size()      ) ,				[self.batch_size, self.num_labels]      )
       self.check_loss_output(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       Optional[int]:
       UpperCamelCase__         =    self.num_labels
       UpperCamelCase__         =    DebertaVaForTokenClassification(config=snake_case_      )
       model.to(snake_case_      )
       model.eval()
       UpperCamelCase__         =    model(snake_case_ ,				attention_mask=snake_case_ ,				token_type_ids=snake_case_ ,				labels=snake_case_      )
       self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.num_labels)      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       Tuple:
       UpperCamelCase__         =    DebertaVaForQuestionAnswering(config=snake_case_      )
       model.to(snake_case_      )
       model.eval()
       UpperCamelCase__         =    model(
           snake_case_ ,				attention_mask=snake_case_ ,				token_type_ids=snake_case_ ,				start_positions=snake_case_ ,				end_positions=snake_case_ ,				)
       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 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       int:
       UpperCamelCase__         =    DebertaVaForMultipleChoice(config=snake_case_      )
       model.to(snake_case_      )
       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(
           snake_case_ ,				attention_mask=snake_case_ ,				token_type_ids=snake_case_ ,				labels=snake_case_ ,				)
       self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_choices)      )







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       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    __lowerCamelCase    (  _a							,	_a							,	unittest.TestCase  ):
   a      :				Any 						=(
       (
           DebertaVaModel,
           DebertaVaForMaskedLM,
           DebertaVaForSequenceClassification,
           DebertaVaForTokenClassification,
           DebertaVaForQuestionAnswering,
           DebertaVaForMultipleChoice,
       )
       if is_torch_available()
       else ()
   )
   a      :				Dict 						=(
       {
           """feature-extraction""": DebertaVaModel,
           """fill-mask""": DebertaVaForMaskedLM,
           """question-answering""": DebertaVaForQuestionAnswering,
           """text-classification""": DebertaVaForSequenceClassification,
           """token-classification""": DebertaVaForTokenClassification,
           """zero-shot""": DebertaVaForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )

   a      :				Tuple 						=True
   a      :				Union[str, Any] 						=False
   a      :				Tuple 						=False
   a      :				Union[str, Any] 						=False
   a      :				Dict 						=False

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       UpperCamelCase__         =    DebertaVaModelTester(self      )
       UpperCamelCase__         =    ConfigTester(self ,				config_class=snake_case_ ,				hidden_size=37      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       self.config_tester.run_common_tests()

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_model(*snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_sequence_classification(*snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_masked_lm(*snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_question_answering(*snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_token_classification(*snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_multiple_choice(*snake_case_      )







   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase__         =    DebertaVaModel.from_pretrained(snake_case_      )
           self.assertIsNotNone(snake_case_      )







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

   @unittest.skip(reason='Model not available yet'      )
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       pass







   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       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(snake_case_ ,				attention_mask=snake_case_      )[0]
       # compare the actual values for a slice.
       UpperCamelCase__         =    torch.tensor(
           [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]]      )
       self.assertTrue(torch.allclose(output[:, 1:4, 1:4] ,				snake_case_ ,				atol=1E-4      ) ,				F'{output[:, 1:4, 1:4]}'      )




