


import unittest

from transformers import DebertaConfig, 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 (
        DebertaForMaskedLM,
        DebertaForQuestionAnswering,
        DebertaForSequenceClassification,
        DebertaForTokenClassification,
        DebertaModel,
    )
    from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST


class      lowercase__	(       __SCREAMING_SNAKE_CASE		):






  def __init__(       self   :       int		,    _lowercase   :       Tuple		,    _lowercase   :       List[Any]=13		,    _lowercase   :       Optional[int]=7		,    _lowercase   :       Union[str, Any]=True		,    _lowercase   :       str=True		,    _lowercase   :       Optional[int]=True		,    _lowercase   :       Optional[int]=True		,    _lowercase   :       Optional[int]=99		,    _lowercase   :       int=32		,    _lowercase   :       Union[str, Any]=5		,    _lowercase   :       str=4		,    _lowercase   :       Any=37		,    _lowercase   :       Tuple="gelu"		,    _lowercase   :       Union[str, Any]=0.1		,    _lowercase   :       List[Any]=0.1		,    _lowercase   :       str=5_12		,    _lowercase   :       Optional[int]=16		,    _lowercase   :       List[Any]=2		,    _lowercase   :       Union[str, Any]=0.0_2		,    _lowercase   :       List[Any]=False		,    _lowercase   :       int=True		,    _lowercase   :       int="None"		,    _lowercase   :       Union[str, Any]=3		,    _lowercase   :       Tuple=4		,    _lowercase   :       Optional[int]=None		,    ):




         """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       _UpperCAmelCase	(       self   :       int			):




         """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       _UpperCAmelCase	(       self   :       int			):




         """simple docstring"""







         return DebertaConfig(
             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   :       int			):




         """simple docstring"""







         UpperCAmelCase__					    =  self.get_config()
         UpperCAmelCase__					    =  3_00
         return config






  def       _UpperCAmelCase	(       self   :       int		,    _lowercase   :       Optional[int]			):




         """simple docstring"""







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






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




         """simple docstring"""







         UpperCAmelCase__					    =  DebertaModel(config=_lowercase			)
         model.to(_lowercase			)
         model.eval()
         UpperCAmelCase__					    =  model(_lowercase		,    attention_mask=_lowercase		,    token_type_ids=_lowercase			)[0]
         UpperCAmelCase__					    =  model(_lowercase		,    token_type_ids=_lowercase			)[0]
         UpperCAmelCase__					    =  model(_lowercase			)[0]

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






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




         """simple docstring"""







         UpperCAmelCase__					    =  DebertaForMaskedLM(config=_lowercase			)
         model.to(_lowercase			)
         model.eval()
         UpperCAmelCase__					    =  model(_lowercase		,    attention_mask=_lowercase		,    token_type_ids=_lowercase		,    labels=_lowercase			)

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






  def       _UpperCAmelCase	(       self   :       Optional[Any]		,    _lowercase   :       Any		,    _lowercase   :       Optional[int]		,    _lowercase   :       str		,    _lowercase   :       str		,    _lowercase   :       Any		,    _lowercase   :       Tuple		,    _lowercase   :       Any			):




         """simple docstring"""







         UpperCAmelCase__					    =  self.num_labels
         UpperCAmelCase__					    =  DebertaForSequenceClassification(_lowercase			)
         model.to(_lowercase			)
         model.eval()
         UpperCAmelCase__					    =  model(_lowercase		,    attention_mask=_lowercase		,    token_type_ids=_lowercase		,    labels=_lowercase			)
         self.parent.assertListEqual(list(result.logits.size()			)		,    [self.batch_size, self.num_labels]			)
         self.check_loss_output(_lowercase			)






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




         """simple docstring"""







         UpperCAmelCase__					    =  self.num_labels
         UpperCAmelCase__					    =  DebertaForTokenClassification(config=_lowercase			)
         model.to(_lowercase			)
         model.eval()
         UpperCAmelCase__					    =  model(_lowercase		,    attention_mask=_lowercase		,    token_type_ids=_lowercase		,    labels=_lowercase			)
         self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.seq_length, self.num_labels)			)






  def       _UpperCAmelCase	(       self   :       List[Any]		,    _lowercase   :       List[str]		,    _lowercase   :       Dict		,    _lowercase   :       int		,    _lowercase   :       Any		,    _lowercase   :       List[str]		,    _lowercase   :       Tuple		,    _lowercase   :       List[Any]			):




         """simple docstring"""







         UpperCAmelCase__					    =  DebertaForQuestionAnswering(config=_lowercase			)
         model.to(_lowercase			)
         model.eval()
         UpperCAmelCase__					    =  model(
             _lowercase		,    attention_mask=_lowercase		,    token_type_ids=_lowercase		,    start_positions=_lowercase		,    end_positions=_lowercase		,    )
         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   :       Dict			):




         """simple docstring"""







         UpperCAmelCase__					    =  self.prepare_config_and_inputs()
         (
             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      lowercase__	(       __SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				unittest.TestCase		):
  A__=	(
      (
          DebertaModel,
          DebertaForMaskedLM,
          DebertaForSequenceClassification,
          DebertaForTokenClassification,
          DebertaForQuestionAnswering,
      )
      if is_torch_available()
      else ()
  )
  A__=	(
      {
          '''feature-extraction''': DebertaModel,
          '''fill-mask''': DebertaForMaskedLM,
          '''question-answering''': DebertaForQuestionAnswering,
          '''text-classification''': DebertaForSequenceClassification,
          '''token-classification''': DebertaForTokenClassification,
          '''zero-shot''': DebertaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )

  A__=	True
  A__=	False
  A__=	False
  A__=	False
  A__=	False






  def       _UpperCAmelCase	(       self   :       Dict			):




         """simple docstring"""







         UpperCAmelCase__					    =  DebertaModelTester(self			)
         UpperCAmelCase__					    =  ConfigTester(self		,    config_class=_lowercase		,    hidden_size=37			)






  def       _UpperCAmelCase	(       self   :       Optional[Any]			):




         """simple docstring"""







         self.config_tester.run_common_tests()






  def       _UpperCAmelCase	(       self   :       int			):




         """simple docstring"""







         UpperCAmelCase__					    =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_model(*_lowercase			)






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




         """simple docstring"""







         UpperCAmelCase__					    =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_sequence_classification(*_lowercase			)






  def       _UpperCAmelCase	(       self   :       List[Any]			):




         """simple docstring"""







         UpperCAmelCase__					    =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_masked_lm(*_lowercase			)






  def       _UpperCAmelCase	(       self   :       Optional[int]			):




         """simple docstring"""







         UpperCAmelCase__					    =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_question_answering(*_lowercase			)






  def       _UpperCAmelCase	(       self   :       List[Any]			):




         """simple docstring"""







         UpperCAmelCase__					    =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_token_classification(*_lowercase			)






  @slow
  def       _UpperCAmelCase	(       self   :       Any			):




         """simple docstring"""







         for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase__					    =  DebertaModel.from_pretrained(_lowercase			)
                self.assertIsNotNone(_lowercase			)




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






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




         """simple docstring"""







         pass






  @slow
  def       _UpperCAmelCase	(       self   :       str			):




         """simple docstring"""







         UpperCAmelCase__					    =  DebertaModel.from_pretrained("microsoft/deberta-base"			)

         UpperCAmelCase__					    =  torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]			)
         UpperCAmelCase__					    =  torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
         with torch.no_grad():
                UpperCAmelCase__					    =  model(_lowercase		,    attention_mask=_lowercase			)[0]
         # compare the actual values for a slice.
         UpperCAmelCase__					    =  torch.tensor(
             [[[-0.5_9_8_6, -0.8_0_5_5, -0.8_4_6_2], [1.4_4_8_4, -0.9_3_4_8, -0.8_0_5_9], [0.3_1_2_3, 0.0_0_3_2, -1.4_1_3_1]]]			)
         self.assertTrue(torch.allclose(output[:, 1:4, 1:4]		,    _lowercase		,    atol=1E-4			)		,    F"""{output[:, 1:4, 1:4]}"""			)











'''simple docstring'''



class 							_a				:





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						val
             SCREAMING_SNAKE_CASE   :      Any    					=						None
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						None







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             if self.val:
                   if val < self.val:
                         if self.left is None:
                               SCREAMING_SNAKE_CASE   :      Optional[int]    					=						Node(A  )
                         else:
                               self.left.insert(A  )
                   elif val > self.val:
                         if self.right is None:
                               SCREAMING_SNAKE_CASE   :      int    					=						Node(A  )
                         else:
                               self.right.insert(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      int    					=						val




def   lowercase__(			__UpperCamelCase:					Optional[int]      ,__UpperCamelCase:					List[str]    ):

      """simple docstring"""






      if root:
            inorder(root.left      ,__UpperCamelCase    )
            res.append(root.val    )
            inorder(root.right      ,__UpperCamelCase    )




def   lowercase__(			__UpperCamelCase:					List[Any]    ):

      """simple docstring"""






      if len(__UpperCamelCase    ) == 0:
            return arr
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						Node(arr[0]    )
      for i in range(1      ,len(__UpperCamelCase    )    ):
            root.insert(arr[i]    )
      # Traverse BST in order.
      SCREAMING_SNAKE_CASE   :      Dict    					=						[]
      inorder(__UpperCamelCase      ,__UpperCamelCase    )
      return res


if __name__ == "__main__":
      print(tree_sort([1_0, 1, 3, 2, 9, 1_4, 1_3]))


