


"""simple docstring"""
import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch



class 			_UpperCAmelCase (							unittest.TestCase				):






		'''simple docstring'''

		lowerCamelCase__				=JukeboxTokenizer
		lowerCamelCase__				={
		    'artist': 'Zac Brown Band',
		    'genres': 'Country',
		    'lyrics': 'I met a traveller from an antique land,\n        Who said "Two vast and trunkless legs of stone\n        Stand in the desert. . . . Near them, on the sand,\n        Half sunk a shattered visage lies, whose frown,\n        And wrinkled lip, and sneer of cold command,\n        Tell that its sculptor well those passions read\n        Which yet survive, stamped on these lifeless things,\n        The hand that mocked them, and the heart that fed;\n        And on the pedestal, these words appear:\n        My name is Ozymandias, King of Kings;\n        Look on my Works, ye Mighty, and despair!\n        Nothing beside remains. Round the decay\n        Of that colossal Wreck, boundless and bare\n        The lone and level sands stretch far away\n        ',
		}



		@require_torch
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			import torch

			__snake_case	:						str						=				JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics'''					)
			__snake_case	:						List[Any]						=				tokenizer(**self.metas					)['''input_ids''']
			# fmt: off
			__snake_case	:						Optional[int]						=				[
			    torch.tensor([[
			        0, 0, 0, 71_69, 5_07, 9, 76, 39, 31, 46, 76, 27,
			        76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
			        44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
			        47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
			        76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
			        30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
			        27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
			        45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
			        41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
			        19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
			        76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
			        76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
			        64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
			        30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
			        27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
			        34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
			        27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
			        41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
			        76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
			        44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
			        76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
			        32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
			        40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
			        20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
			        45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
			        31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
			        45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
			        76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
			        34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
			        31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
			        40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
			        38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
			        78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
			        76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
			        41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
			        27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
			        46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
			        76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
			        41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
			        46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
			        41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
			        78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
			        40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
			        27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
			        76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
			        76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
			        41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
			        76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
			        27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
			        78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
			        34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
			        44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
			        40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
			        78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
			        46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
			        38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
			        40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
			        27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
			        20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
			        76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
			        76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
			        76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
			        76, 76]]					),
			    torch.tensor([[0, 0, 0, 10_69, 11]]					),
			    torch.tensor([[0, 0, 0, 10_69, 11]]					),
			]
			# fmt: on
			self.assertTrue(torch.allclose(tokens[0]				,       EXPECTED_OUTPUT[0]					)					)
			self.assertTrue(torch.allclose(tokens[1]				,       EXPECTED_OUTPUT[1]					)					)
			self.assertTrue(torch.allclose(tokens[2]				,       EXPECTED_OUTPUT[2]					)					)



		@require_torch
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			import torch

			__snake_case	:						List[Any]						=				JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics'''					)
			__snake_case	:						Optional[int]						=				tokenizer(**self.metas					)['''input_ids''']
			# fmt: off
			__snake_case	:						Any						=				[
			    torch.tensor([[
			        0, 0, 0, 10_69, 11, -1, -1, -1, -1, 9, 77, 39,
			        31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
			        31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
			        40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
			        79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
			        77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
			        27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
			        37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
			        32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
			        77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
			        77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
			        77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
			        46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
			        77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
			        77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
			        77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
			        77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
			        64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
			        40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
			        40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
			        38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
			        31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
			        41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
			        77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
			        46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
			        41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
			        31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
			        31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
			        23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
			        44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
			        31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
			        38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
			        40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
			        77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
			        27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
			        31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
			        34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
			        31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
			        1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
			        31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
			        45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
			        31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
			        77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
			        15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
			        11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
			        45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
			        41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
			        44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
			        46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
			        27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
			        77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
			        35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
			        77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
			        31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
			        77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
			        41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
			        77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
			        40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
			        77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
			        77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
			        27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
			        77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
			        77, 77, 77, 77, 77, 77]]					),
			    torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]					),
			    torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]					),
			]
			# fmt: on
			self.assertTrue(torch.allclose(tokens[0]				,       EXPECTED_OUTPUT[0]					)					)
			self.assertTrue(torch.allclose(tokens[1]				,       EXPECTED_OUTPUT[1]					)					)
			self.assertTrue(torch.allclose(tokens[2]				,       EXPECTED_OUTPUT[2]					)					)









'''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				:    Optional[Any]						,						_A				:    Optional[Any]						,						_A				:    List[str]=13						,						_A				:    Any=7						,						_A				:    str=True						,						_A				:    Any=True						,						_A				:    Any=True						,						_A				:    Optional[int]=True						,						_A				:    int=99						,						_A				:    Optional[int]=32						,						_A				:    List[Any]=5						,						_A				:    Optional[Any]=4						,						_A				:    Dict=37						,						_A				:    Any="gelu"						,						_A				:    str=0.1						,						_A				:    int=0.1						,						_A				:    Optional[Any]=512						,						_A				:    Optional[Any]=16						,						_A				:    List[Any]=2						,						_A				:    str=0.0_2						,						_A				:    Optional[Any]=False						,						_A				:    Any=True						,						_A				:    Dict="None"						,						_A				:    List[str]=3						,						_A				:    List[str]=4						,						_A				:    Tuple=None						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		Tuple													=				parent
          UpperCAmelCase__			:		Union[str, Any]													=				batch_size
          UpperCAmelCase__			:		Dict													=				seq_length
          UpperCAmelCase__			:		Dict													=				is_training
          UpperCAmelCase__			:		Optional[Any]													=				use_input_mask
          UpperCAmelCase__			:		Optional[Any]													=				use_token_type_ids
          UpperCAmelCase__			:		Union[str, Any]													=				use_labels
          UpperCAmelCase__			:		Tuple													=				vocab_size
          UpperCAmelCase__			:		Tuple													=				hidden_size
          UpperCAmelCase__			:		Any													=				num_hidden_layers
          UpperCAmelCase__			:		Tuple													=				num_attention_heads
          UpperCAmelCase__			:		List[Any]													=				intermediate_size
          UpperCAmelCase__			:		Union[str, Any]													=				hidden_act
          UpperCAmelCase__			:		Any													=				hidden_dropout_prob
          UpperCAmelCase__			:		Any													=				attention_probs_dropout_prob
          UpperCAmelCase__			:		int													=				max_position_embeddings
          UpperCAmelCase__			:		Optional[int]													=				type_vocab_size
          UpperCAmelCase__			:		Union[str, Any]													=				type_sequence_label_size
          UpperCAmelCase__			:		int													=				initializer_range
          UpperCAmelCase__			:		Any													=				num_labels
          UpperCAmelCase__			:		Optional[Any]													=				num_choices
          UpperCAmelCase__			:		List[Any]													=				relative_attention
          UpperCAmelCase__			:		int													=				position_biased_input
          UpperCAmelCase__			:		str													=				pos_att_type
          UpperCAmelCase__			:		Union[str, Any]													=				scope






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				ids_tensor([self.batch_size, self.seq_length]						,						self.vocab_size )

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

          UpperCAmelCase__			:		Optional[Any]													=				None
          if self.use_token_type_ids:
              UpperCAmelCase__			:		List[str]													=				ids_tensor([self.batch_size, self.seq_length]						,						self.type_vocab_size )

          UpperCAmelCase__			:		Dict													=				None
          UpperCAmelCase__			:		Any													=				None
          UpperCAmelCase__			:		Dict													=				None
          if self.use_labels:
              UpperCAmelCase__			:		int													=				ids_tensor([self.batch_size]						,						self.type_sequence_label_size )
              UpperCAmelCase__			:		List[str]													=				ids_tensor([self.batch_size, self.seq_length]						,						self.num_labels )
              UpperCAmelCase__			:		Dict													=				ids_tensor([self.batch_size]						,						self.num_choices )

          UpperCAmelCase__			:		Optional[Any]													=				self.get_config()

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






      def lowercase_							(					self				:    List[str] ):


          '''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 lowercase_							(					self				:    Dict						,						_A				:    Optional[int] ):


          '''simple docstring'''


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






      def lowercase_							(					self				:    int						,						_A				:    int						,						_A				:    Any						,						_A				:    Tuple						,						_A				:    List[Any]						,						_A				:    str						,						_A				:    Union[str, Any]						,						_A				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				DebertaVaModel(config=_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		str													=				model(_A						,						attention_mask=_A						,						token_type_ids=_A )[0]
          UpperCAmelCase__			:		List[str]													=				model(_A						,						token_type_ids=_A )[0]
          UpperCAmelCase__			:		List[Any]													=				model(_A )[0]

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






      def lowercase_							(					self				:    Optional[Any]						,						_A				:    Tuple						,						_A				:    List[Any]						,						_A				:    Optional[Any]						,						_A				:    int						,						_A				:    List[Any]						,						_A				:    Optional[int]						,						_A				:    str ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				DebertaVaForMaskedLM(config=_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		Any													=				model(_A						,						attention_mask=_A						,						token_type_ids=_A						,						labels=_A )

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






      def lowercase_							(					self				:    str						,						_A				:    str						,						_A				:    Any						,						_A				:    Any						,						_A				:    List[Any]						,						_A				:    Dict						,						_A				:    Tuple						,						_A				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				self.num_labels
          UpperCAmelCase__			:		Union[str, Any]													=				DebertaVaForSequenceClassification(_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		str													=				model(_A						,						attention_mask=_A						,						token_type_ids=_A						,						labels=_A )
          self.parent.assertListEqual(list(result.logits.size() )						,						[self.batch_size, self.num_labels] )
          self.check_loss_output(_A )






      def lowercase_							(					self				:    Any						,						_A				:    List[str]						,						_A				:    List[str]						,						_A				:    Optional[int]						,						_A				:    Tuple						,						_A				:    Dict						,						_A				:    List[str]						,						_A				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				self.num_labels
          UpperCAmelCase__			:		int													=				DebertaVaForTokenClassification(config=_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		List[Any]													=				model(_A						,						attention_mask=_A						,						token_type_ids=_A						,						labels=_A )
          self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.seq_length, self.num_labels) )






      def lowercase_							(					self				:    str						,						_A				:    List[str]						,						_A				:    str						,						_A				:    Optional[int]						,						_A				:    Optional[int]						,						_A				:    Union[str, Any]						,						_A				:    Dict						,						_A				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				DebertaVaForQuestionAnswering(config=_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		int													=				model(
              _A						,						attention_mask=_A						,						token_type_ids=_A						,						start_positions=_A						,						end_positions=_A						,						)
          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 lowercase_							(					self				:    Any						,						_A				:    Tuple						,						_A				:    Optional[int]						,						_A				:    Optional[int]						,						_A				:    str						,						_A				:    List[str]						,						_A				:    Any						,						_A				:    int ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				DebertaVaForMultipleChoice(config=_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		Optional[Any]													=				input_ids.unsqueeze(1 ).expand(-1						,						self.num_choices						,						-1 ).contiguous()
          UpperCAmelCase__			:		str													=				token_type_ids.unsqueeze(1 ).expand(-1						,						self.num_choices						,						-1 ).contiguous()
          UpperCAmelCase__			:		Dict													=				input_mask.unsqueeze(1 ).expand(-1						,						self.num_choices						,						-1 ).contiguous()
          UpperCAmelCase__			:		List[str]													=				model(
              _A						,						attention_mask=_A						,						token_type_ids=_A						,						labels=_A						,						)
          self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.num_choices) )






      def lowercase_							(					self				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				self.prepare_config_and_inputs()
          (
              (
              UpperCAmelCase__
          )						,						(
              UpperCAmelCase__
          )						,						(
              UpperCAmelCase__
          )						,						(
              UpperCAmelCase__
          )						,						(
              UpperCAmelCase__
          )						,						(
              UpperCAmelCase__
          )						,						(
              UpperCAmelCase__
          )						,						
          )			:		Any													=				config_and_inputs
          UpperCAmelCase__			:		Optional[int]													=				{'''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      ):
      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 lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				DebertaVaModelTester(self )
          UpperCAmelCase__			:		Tuple													=				ConfigTester(self						,						config_class=_A						,						hidden_size=37 )






      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          self.config_tester.run_common_tests()






      def lowercase_							(					self				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Any													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_model(*_A )






      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_sequence_classification(*_A )






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_masked_lm(*_A )






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_question_answering(*_A )






      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_token_classification(*_A )






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Union[str, Any]													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_multiple_choice(*_A )






      @slow
      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase__			:		List[str]													=				DebertaVaModel.from_pretrained(_A )
              self.assertIsNotNone(_A )




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






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


          '''simple docstring'''


          pass






      @slow
      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Tuple													=				DebertaVaModel.from_pretrained('''microsoft/deberta-v2-xlarge''' )

          UpperCAmelCase__			:		List[Any]													=				torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
          UpperCAmelCase__			:		List[str]													=				torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
          with torch.no_grad():
              UpperCAmelCase__			:		List[str]													=				model(_A						,						attention_mask=_A )[0]
          # compare the actual values for a slice.
          UpperCAmelCase__			:		str													=				torch.tensor(
              [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]] )
          self.assertTrue(torch.allclose(output[:, 1:4, 1:4]						,						_A						,						atol=1e-4 )						,						f"""{output[:, 1:4, 1:4]}""" )
