



"""simple docstring"""
from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import (
           TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
           TFTransfoXLForSequenceClassification,
           TFTransfoXLLMHeadModel,
           TFTransfoXLModel,
       )


class    SCREAMING_SNAKE_CASE__ :

    def __init__(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				Union[str, Any] ,  ):







      """simple docstring"""







      lowercase_								= parent
      lowercase_								= 1_3
      lowercase_								= 7
      lowercase_								= 3_0
      lowercase_								= self.seq_length + self.mem_len
      lowercase_								= 1_5
      lowercase_								= True
      lowercase_								= True
      lowercase_								= 9_9
      lowercase_								= [1_0, 5_0, 8_0]
      lowercase_								= 3_2
      lowercase_								= 3_2
      lowercase_								= 4
      lowercase_								= 8
      lowercase_								= 1_2_8
      lowercase_								= 2
      lowercase_								= 2
      lowercase_								= None
      lowercase_								= 1
      lowercase_								= 0
      lowercase_								= 3
      lowercase_								= self.vocab_size - 1
      lowercase_								= 0.01

    def 					_UpperCAmelCase	(				self			:				Optional[int]):







      """simple docstring"""







      lowercase_								= ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size)
      lowercase_								= ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size)

      lowercase_								= None
      if self.use_labels:
        lowercase_								= ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size)

      lowercase_								= TransfoXLConfig(
          vocab_size=self.vocab_size ,  mem_len=self.mem_len ,  clamp_len=self.clamp_len ,  cutoffs=self.cutoffs ,  d_model=self.hidden_size ,  d_embed=self.d_embed ,  n_head=self.num_attention_heads ,  d_head=self.d_head ,  d_inner=self.d_inner ,  div_val=self.div_val ,  n_layer=self.num_hidden_layers ,  eos_token_id=self.eos_token_id ,  pad_token_id=self.vocab_size - 1 ,  init_range=self.init_range ,  num_labels=self.num_labels ,  )

      return (config, input_ids_a, input_ids_a, lm_labels)

    def 					_UpperCAmelCase	(				self			:				List[Any]):







      """simple docstring"""







      random.seed(self.seed)
      tf.random.set_seed(self.seed)

    def 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				Union[str, Any] ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				Optional[Any]):







      """simple docstring"""







      lowercase_								= TFTransfoXLModel(UpperCamelCase__)

      lowercase_					,    lowercase_								= model(UpperCamelCase__).to_tuple()

      lowercase_								= {"""input_ids""": input_ids_a, """mems""": mems_a}

      lowercase_					,    lowercase_								= model(UpperCamelCase__).to_tuple()

      self.parent.assertEqual(hidden_states_a.shape ,  (self.batch_size, self.seq_length, self.hidden_size))
      self.parent.assertEqual(hidden_states_a.shape ,  (self.batch_size, self.seq_length, self.hidden_size))
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a] ,  [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,  )
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a] ,  [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,  )

    def 					_UpperCAmelCase	(				self			:				Optional[Any] ,  lowerCAmelCase_			:				Dict ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				Tuple ,  lowerCAmelCase_			:				Dict):







      """simple docstring"""







      lowercase_								= TFTransfoXLLMHeadModel(UpperCamelCase__)

      lowercase_					,    lowercase_								= model(UpperCamelCase__).to_tuple()

      lowercase_								= {"""input_ids""": input_ids_a, """labels""": lm_labels}
      lowercase_					,    lowercase_								= model(UpperCamelCase__).to_tuple()

      lowercase_					,    lowercase_								= model([input_ids_a, mems_a]).to_tuple()

      lowercase_								= {"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}

      lowercase_					,    lowercase_								= model(UpperCamelCase__).to_tuple()

      self.parent.assertEqual(lm_logits_a.shape ,  (self.batch_size, self.seq_length, self.vocab_size))
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a] ,  [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,  )

      self.parent.assertEqual(lm_logits_a.shape ,  (self.batch_size, self.seq_length, self.vocab_size))
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a] ,  [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,  )

    def 					_UpperCAmelCase	(				self			:				Optional[Any] ,  lowerCAmelCase_			:				Any ,  lowerCAmelCase_			:				Tuple ,  lowerCAmelCase_			:				Optional[Any] ,  lowerCAmelCase_			:				List[str]):







      """simple docstring"""







      lowercase_								= TFTransfoXLForSequenceClassification(UpperCamelCase__)
      lowercase_								= model(UpperCamelCase__)
      self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_labels))




    def 					_UpperCAmelCase	(				self			:				Tuple):







      """simple docstring"""







      lowercase_								= self.prepare_config_and_inputs()
      ((lowercase_)					,    (lowercase_)					,    (lowercase_)					,    (lowercase_))								= config_and_inputs
      lowercase_								= {"""input_ids""": input_ids_a}
      return config, inputs_dict







@require_tf
class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase      ,  __UpperCAmelCase      ,  unittest.TestCase   ):
    lowercase__      =   (
        (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
    )
    lowercase__      =   () if is_tf_available() else ()
    lowercase__      =   (
        {
            "feature-extraction": TFTransfoXLModel,
            "text-classification": TFTransfoXLForSequenceClassification,
            "text-generation": TFTransfoXLLMHeadModel,
            "zero-shot": TFTransfoXLForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
    lowercase__      =   False
    lowercase__      =   False
    lowercase__      =   False
    lowercase__      =   False

    def 					_UpperCAmelCase	(				self			:				List[str] ,  lowerCAmelCase_			:				List[Any] ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				List[Any] ,  lowerCAmelCase_			:				Any ,  lowerCAmelCase_			:				List[Any]):







      """simple docstring"""







      if pipeline_test_casse_name == "TextGenerationPipelineTests":
        # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
        # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
        # tokenizer.
        return True

      return False

    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







      lowercase_								= TFTransfoXLModelTester(self)
      lowercase_								= ConfigTester(self ,  config_class=UpperCamelCase__ ,  d_embed=3_7)

    def 					_UpperCAmelCase	(				self			:				Optional[Any]):







      """simple docstring"""







      self.config_tester.run_common_tests()

    def 					_UpperCAmelCase	(				self			:				str):







      """simple docstring"""







      self.model_tester.set_seed()
      lowercase_								= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_model(*UpperCamelCase__)

    def 					_UpperCAmelCase	(				self			:				str):







      """simple docstring"""







      self.model_tester.set_seed()
      lowercase_								= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCamelCase__)

    def 					_UpperCAmelCase	(				self			:				Optional[Any]):







      """simple docstring"""







      lowercase_								= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCamelCase__)

    def 					_UpperCAmelCase	(				self			:				List[str]):







      """simple docstring"""







      lowercase_					,    lowercase_								= self.model_tester.prepare_config_and_inputs_for_common()
      lowercase_								= [TFTransfoXLForSequenceClassification]

      for model_class in self.all_model_classes:
        lowercase_								= model_class(UpperCamelCase__)
        assert isinstance(model.get_input_embeddings() ,  tf.keras.layers.Layer)
        if model_class in list_other_models_with_output_ebd:
          lowercase_								= model.get_output_embeddings()
          assert isinstance(UpperCamelCase__ ,  tf.keras.layers.Layer)
          lowercase_								= model.get_bias()
          assert name is None
        else:
          lowercase_								= model.get_output_embeddings()
          assert x is None
          lowercase_								= model.get_bias()
          assert name is None

    def 					_UpperCAmelCase	(				self			:				Union[str, Any]):







      """simple docstring"""







      pass

    @slow
    def 					_UpperCAmelCase	(				self			:				Tuple):







      """simple docstring"""







      for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowercase_								= TFTransfoXLModel.from_pretrained(UpperCamelCase__)
        self.assertIsNotNone(UpperCamelCase__)




    @unittest.skip(reason="""This model doesn\'t play well with fit() due to not returning a single loss.""")
    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







      pass







@require_tf
class    SCREAMING_SNAKE_CASE__ (							unittest.TestCase   ):

    @unittest.skip("""Skip test until #12651 is resolved.""")
    @slow
    def 					_UpperCAmelCase	(				self			:				Optional[Any]):







      """simple docstring"""







      lowercase_								= TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103""")
      # fmt: off
      lowercase_								= tf.convert_to_tensor([[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0]] ,  dtype=tf.intaa)  # noqa: E231
      # fmt: on
      #  In 1991 , the remains of Russian Tsar Nicholas II and his family
      #  ( except for Alexei and Maria ) are discovered .
      #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
      #  remainder of the story . 1883 Western Siberia ,
      #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
      #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
      #  father initially slaps him for making such an accusation , Rasputin watches as the
      #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
      #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
      #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

      # fmt: off
      lowercase_								= [3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0,3_3,1,1_8_5_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_8,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,0]  # noqa: E231
      # fmt: on
      #  In 1991, the remains of Russian Tsar Nicholas II and his family (
      #  except for Alexei and Maria ) are discovered. The voice of young son,
      #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
      #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
      #  and a group of men to perform magic. Rasputin has a vision and
      #  denounces one of the men as a horse thief. Although his father initially
      #  slaps him for making such an accusation, Rasputin watches as the man
      #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
      #  of the Virgin Mary, prompting him to become a priest.
      #  Rasputin quickly becomes famous, with people, even a bishop, begging for
      #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
      # Nicholas II and his family were discovered. The voice of <unk> young son,
      # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

      lowercase_								= model.generate(UpperCamelCase__ ,  max_length=2_0_0 ,  do_sample=UpperCamelCase__)
      self.assertListEqual(output_ids[0].numpy().tolist() ,  UpperCamelCase__)






"""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    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):

    def __init__(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				Optional[int]=1_3 ,  lowerCAmelCase_			:				Union[str, Any]=7 ,  lowerCAmelCase_			:				int=True ,  lowerCAmelCase_			:				Optional[int]=True ,  lowerCAmelCase_			:				List[Any]=True ,  lowerCAmelCase_			:				Tuple=True ,  lowerCAmelCase_			:				Dict=9_9 ,  lowerCAmelCase_			:				List[str]=3_2 ,  lowerCAmelCase_			:				Tuple=5 ,  lowerCAmelCase_			:				str=4 ,  lowerCAmelCase_			:				List[str]=3_7 ,  lowerCAmelCase_			:				List[str]="gelu" ,  lowerCAmelCase_			:				Optional[Any]=0.1 ,  lowerCAmelCase_			:				Union[str, Any]=0.1 ,  lowerCAmelCase_			:				List[str]=5_1_2 ,  lowerCAmelCase_			:				Union[str, Any]=1_6 ,  lowerCAmelCase_			:				List[Any]=2 ,  lowerCAmelCase_			:				Dict=0.02 ,  lowerCAmelCase_			:				int=False ,  lowerCAmelCase_			:				Any=True ,  lowerCAmelCase_			:				Tuple="None" ,  lowerCAmelCase_			:				int=3 ,  lowerCAmelCase_			:				Union[str, Any]=4 ,  lowerCAmelCase_			:				Dict=None ,  ):







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				Any):







      """simple docstring"""







      lowercase_								= ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size)

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

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

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

      lowercase_								= self.get_config()

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

    def 					_UpperCAmelCase	(				self			:				int):







      """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 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				Dict):







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				Any ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				Optional[Any] ,  lowerCAmelCase_			:				List[Any] ,  lowerCAmelCase_			:				Dict ,  lowerCAmelCase_			:				Tuple ,  lowerCAmelCase_			:				Union[str, Any] ,  lowerCAmelCase_			:				List[str]):







      """simple docstring"""







      lowercase_								= DebertaVaModel(config=lowerCAmelCase_)
      model.to(lowerCAmelCase_)
      model.eval()
      lowercase_								= model(lowerCAmelCase_ ,  attention_mask=lowerCAmelCase_ ,  token_type_ids=lowerCAmelCase_)[0]
      lowercase_								= model(lowerCAmelCase_ ,  token_type_ids=lowerCAmelCase_)[0]
      lowercase_								= model(lowerCAmelCase_)[0]

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

    def 					_UpperCAmelCase	(				self			:				Optional[Any] ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				Tuple ,  lowerCAmelCase_			:				Dict ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				Union[str, Any]):







      """simple docstring"""







      lowercase_								= DebertaVaForMaskedLM(config=lowerCAmelCase_)
      model.to(lowerCAmelCase_)
      model.eval()
      lowercase_								= 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 					_UpperCAmelCase	(				self			:				Any ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				List[Any] ,  lowerCAmelCase_			:				Optional[Any] ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				Tuple ,  lowerCAmelCase_			:				Tuple):







      """simple docstring"""







      lowercase_								= self.num_labels
      lowercase_								= DebertaVaForSequenceClassification(lowerCAmelCase_)
      model.to(lowerCAmelCase_)
      model.eval()
      lowercase_								= 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 					_UpperCAmelCase	(				self			:				List[str] ,  lowerCAmelCase_			:				Any ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				Union[str, Any] ,  lowerCAmelCase_			:				Tuple ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				Union[str, Any] ,  lowerCAmelCase_			:				Any):







      """simple docstring"""







      lowercase_								= self.num_labels
      lowercase_								= DebertaVaForTokenClassification(config=lowerCAmelCase_)
      model.to(lowerCAmelCase_)
      model.eval()
      lowercase_								= 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 					_UpperCAmelCase	(				self			:				str ,  lowerCAmelCase_			:				Tuple ,  lowerCAmelCase_			:				Union[str, Any] ,  lowerCAmelCase_			:				Dict ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				Tuple ,  lowerCAmelCase_			:				Dict):







      """simple docstring"""







      lowercase_								= DebertaVaForQuestionAnswering(config=lowerCAmelCase_)
      model.to(lowerCAmelCase_)
      model.eval()
      lowercase_								= 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 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				Union[str, Any] ,  lowerCAmelCase_			:				Dict ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				Optional[Any]):







      """simple docstring"""







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




    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







      lowercase_								= self.prepare_config_and_inputs()
      (
          (
          lowercase_
      )					,    (
          lowercase_
      )					,    (
          lowercase_
      )					,    (
          lowercase_
      )					,    (
          lowercase_
      )					,    (
          lowercase_
      )					,    (
          lowercase_
      )					,    
      )								= config_and_inputs
      lowercase_								= {"""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   ):
    lowercase__      =   (
        (
            DebertaVaModel,
            DebertaVaForMaskedLM,
            DebertaVaForSequenceClassification,
            DebertaVaForTokenClassification,
            DebertaVaForQuestionAnswering,
            DebertaVaForMultipleChoice,
        )
        if is_torch_available()
        else ()
    )
    lowercase__      =   (
        {
            "feature-extraction": DebertaVaModel,
            "fill-mask": DebertaVaForMaskedLM,
            "question-answering": DebertaVaForQuestionAnswering,
            "text-classification": DebertaVaForSequenceClassification,
            "token-classification": DebertaVaForTokenClassification,
            "zero-shot": DebertaVaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )

    lowercase__      =   True
    lowercase__      =   False
    lowercase__      =   False
    lowercase__      =   False
    lowercase__      =   False

    def 					_UpperCAmelCase	(				self			:				Optional[Any]):







      """simple docstring"""







      lowercase_								= DebertaVaModelTester(self)
      lowercase_								= ConfigTester(self ,  config_class=lowerCAmelCase_ ,  hidden_size=3_7)

    def 					_UpperCAmelCase	(				self			:				Union[str, Any]):







      """simple docstring"""







      self.config_tester.run_common_tests()

    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







      lowercase_								= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_model(*lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Union[str, Any]):







      """simple docstring"""







      lowercase_								= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Dict):







      """simple docstring"""







      lowercase_								= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				List[Any]):







      """simple docstring"""







      lowercase_								= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_question_answering(*lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				str):







      """simple docstring"""







      lowercase_								= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_token_classification(*lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Union[str, Any]):







      """simple docstring"""







      lowercase_								= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_multiple_choice(*lowerCAmelCase_)




    @slow
    def 					_UpperCAmelCase	(				self			:				Tuple):







      """simple docstring"""







      for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowercase_								= DebertaVaModel.from_pretrained(lowerCAmelCase_)
        self.assertIsNotNone(lowerCAmelCase_)







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

    @unittest.skip(reason="""Model not available yet""")
    def 					_UpperCAmelCase	(				self			:				Optional[int]):







      """simple docstring"""







      pass




    @slow
    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







      lowercase_								= DebertaVaModel.from_pretrained("""microsoft/deberta-v2-xlarge""")

      lowercase_								= torch.tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]])
      lowercase_								= torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
      with torch.no_grad():
        lowercase_								= model(lowerCAmelCase_ ,  attention_mask=lowerCAmelCase_)[0]
      # compare the actual values for a slice.
      lowercase_								= 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] ,  lowerCAmelCase_ ,  atol=1E-4) ,  F'''{output[:, 1:4, 1:4]}''')

