





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     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  def __init__(    self			:		Optional[Any] ,  lowerCAmelCase_			:		List[Any] ,  lowerCAmelCase_			:		Optional[Any]=1_3 ,  lowerCAmelCase_			:		Dict=7 ,  lowerCAmelCase_			:		Optional[int]=True ,  lowerCAmelCase_			:		Union[str, Any]=True ,  lowerCAmelCase_			:		Dict=True ,  lowerCAmelCase_			:		Optional[int]=True ,  lowerCAmelCase_			:		List[str]=9_9 ,  lowerCAmelCase_			:		str=3_2 ,  lowerCAmelCase_			:		str=5 ,  lowerCAmelCase_			:		Dict=4 ,  lowerCAmelCase_			:		List[Any]=3_7 ,  lowerCAmelCase_			:		int="gelu" ,  lowerCAmelCase_			:		Tuple=0.1 ,  lowerCAmelCase_			:		Union[str, Any]=0.1 ,  lowerCAmelCase_			:		Tuple=5_1_2 ,  lowerCAmelCase_			:		List[str]=1_6 ,  lowerCAmelCase_			:		List[str]=2 ,  lowerCAmelCase_			:		Tuple=0.02 ,  lowerCAmelCase_			:		int=False ,  lowerCAmelCase_			:		Any=True ,  lowerCAmelCase_			:		int="None" ,  lowerCAmelCase_			:		Optional[Any]=3 ,  lowerCAmelCase_			:		List[str]=4 ,  lowerCAmelCase_			:		Optional[Any]=None ,  )							->						int:
     __lowerCAmelCase							     =     parent
     __lowerCAmelCase							     =     batch_size
     __lowerCAmelCase							     =     seq_length
     __lowerCAmelCase							     =     is_training
     __lowerCAmelCase							     =     use_input_mask
     __lowerCAmelCase							     =     use_token_type_ids
     __lowerCAmelCase							     =     use_labels
     __lowerCAmelCase							     =     vocab_size
     __lowerCAmelCase							     =     hidden_size
     __lowerCAmelCase							     =     num_hidden_layers
     __lowerCAmelCase							     =     num_attention_heads
     __lowerCAmelCase							     =     intermediate_size
     __lowerCAmelCase							     =     hidden_act
     __lowerCAmelCase							     =     hidden_dropout_prob
     __lowerCAmelCase							     =     attention_probs_dropout_prob
     __lowerCAmelCase							     =     max_position_embeddings
     __lowerCAmelCase							     =     type_vocab_size
     __lowerCAmelCase							     =     type_sequence_label_size
     __lowerCAmelCase							     =     initializer_range
     __lowerCAmelCase							     =     num_labels
     __lowerCAmelCase							     =     num_choices
     __lowerCAmelCase							     =     relative_attention
     __lowerCAmelCase							     =     position_biased_input
     __lowerCAmelCase							     =     pos_att_type
     __lowerCAmelCase							     =     scope






  def     lowercase		(    self			:		Optional[int]					)							->						str:
     __lowerCAmelCase							     =     ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size					)

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

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

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

     __lowerCAmelCase							     =     self.get_config()

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






  def     lowercase		(    self			:		Union[str, Any]					)							->						Union[str, Any]:
     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			:		List[str] ,  lowerCAmelCase_			:		str					)							->						Optional[Any]:
     self.parent.assertListEqual(list(result.loss.size()					) ,  []					)






  def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		Union[str, Any] ,  lowerCAmelCase_			:		Union[str, Any] ,  lowerCAmelCase_			:		List[Any] ,  lowerCAmelCase_			:		Optional[Any] ,  lowerCAmelCase_			:		str ,  lowerCAmelCase_			:		Optional[Any] ,  lowerCAmelCase_			:		Any					)							->						Optional[int]:
     __lowerCAmelCase							     =     DebertaVaModel(config=lowerCAmelCase_					)
     model.to(lowerCAmelCase_					)
     model.eval()
     __lowerCAmelCase							     =     model(lowerCAmelCase_ ,  attention_mask=lowerCAmelCase_ ,  token_type_ids=lowerCAmelCase_					)[0]
     __lowerCAmelCase							     =     model(lowerCAmelCase_ ,  token_type_ids=lowerCAmelCase_					)[0]
     __lowerCAmelCase							     =     model(lowerCAmelCase_					)[0]

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






  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		Dict ,  lowerCAmelCase_			:		Optional[Any] ,  lowerCAmelCase_			:		Union[str, Any] ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		Tuple ,  lowerCAmelCase_			:		Optional[Any] ,  lowerCAmelCase_			:		Any					)							->						Any:
     __lowerCAmelCase							     =     DebertaVaForMaskedLM(config=lowerCAmelCase_					)
     model.to(lowerCAmelCase_					)
     model.eval()
     __lowerCAmelCase							     =     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     lowercase		(    self			:		List[str] ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		Optional[Any] ,  lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		str ,  lowerCAmelCase_			:		Union[str, Any]					)							->						List[str]:
     __lowerCAmelCase							     =     self.num_labels
     __lowerCAmelCase							     =     DebertaVaForSequenceClassification(lowerCAmelCase_					)
     model.to(lowerCAmelCase_					)
     model.eval()
     __lowerCAmelCase							     =     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     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		List[Any] ,  lowerCAmelCase_			:		Union[str, Any] ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		Dict ,  lowerCAmelCase_			:		Dict					)							->						Any:
     __lowerCAmelCase							     =     self.num_labels
     __lowerCAmelCase							     =     DebertaVaForTokenClassification(config=lowerCAmelCase_					)
     model.to(lowerCAmelCase_					)
     model.eval()
     __lowerCAmelCase							     =     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     lowercase		(    self			:		int ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		List[Any] ,  lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		List[Any] ,  lowerCAmelCase_			:		Union[str, Any] ,  lowerCAmelCase_			:		Tuple					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     DebertaVaForQuestionAnswering(config=lowerCAmelCase_					)
     model.to(lowerCAmelCase_					)
     model.eval()
     __lowerCAmelCase							     =     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     lowercase		(    self			:		int ,  lowerCAmelCase_			:		Any ,  lowerCAmelCase_			:		str ,  lowerCAmelCase_			:		List[Any] ,  lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		Optional[Any] ,  lowerCAmelCase_			:		Union[str, Any]					)							->						int:
     __lowerCAmelCase							     =     DebertaVaForMultipleChoice(config=lowerCAmelCase_					)
     model.to(lowerCAmelCase_					)
     model.eval()
     __lowerCAmelCase							     =     input_ids.unsqueeze(1					).expand(-1 ,  self.num_choices ,  -1					).contiguous()
     __lowerCAmelCase							     =     token_type_ids.unsqueeze(1					).expand(-1 ,  self.num_choices ,  -1					).contiguous()
     __lowerCAmelCase							     =     input_mask.unsqueeze(1					).expand(-1 ,  self.num_choices ,  -1					).contiguous()
     __lowerCAmelCase							     =     model(
         lowerCAmelCase_ ,  attention_mask=lowerCAmelCase_ ,  token_type_ids=lowerCAmelCase_ ,  labels=lowerCAmelCase_ ,  )
     self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_choices)					)






  def     lowercase		(    self			:		Union[str, Any]					)							->						Optional[Any]:
     __lowerCAmelCase							     =     self.prepare_config_and_inputs()
     (
         (
         __lowerCAmelCase
     )     ,     (
         __lowerCAmelCase
     )     ,     (
         __lowerCAmelCase
     )     ,     (
         __lowerCAmelCase
     )     ,     (
         __lowerCAmelCase
     )     ,     (
         __lowerCAmelCase
     )     ,     (
         __lowerCAmelCase
     )     ,     
     )							     =     config_and_inputs
     __lowerCAmelCase							     =     {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
     return config, inputs_dict





@require_torch
class     _UpperCAmelCase   (   _UpperCamelCase		,   _UpperCamelCase		,   unittest.TestCase   ):

  """simple docstring"""


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

  a_      =			True
  a_      =			False
  a_      =			False
  a_      =			False
  a_      =			False






  def     lowercase		(    self			:		List[str]					)							->						List[Any]:
     __lowerCAmelCase							     =     DebertaVaModelTester(self					)
     __lowerCAmelCase							     =     ConfigTester(self ,  config_class=lowerCAmelCase_ ,  hidden_size=3_7					)






  def     lowercase		(    self			:		int					)							->						Any:
     self.config_tester.run_common_tests()






  def     lowercase		(    self			:		int					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_model(*lowerCAmelCase_					)






  def     lowercase		(    self			:		Union[str, Any]					)							->						Dict:
     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCAmelCase_					)






  def     lowercase		(    self			:		Optional[int]					)							->						Optional[int]:
     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCAmelCase_					)






  def     lowercase		(    self			:		Tuple					)							->						Optional[Any]:
     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_question_answering(*lowerCAmelCase_					)






  def     lowercase		(    self			:		Tuple					)							->						List[str]:
     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_token_classification(*lowerCAmelCase_					)






  def     lowercase		(    self			:		Optional[Any]					)							->						Tuple:
     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_multiple_choice(*lowerCAmelCase_					)






  @slow
  def     lowercase		(    self			:		Dict					)							->						int:
     for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __lowerCAmelCase							     =     DebertaVaModel.from_pretrained(lowerCAmelCase_					)
        self.assertIsNotNone(lowerCAmelCase_					)





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

  """simple docstring"""


  @unittest.skip(reason='Model not available yet'					)
  def     lowercase		(    self			:		List[str]					)							->						Union[str, Any]:
     pass






  @slow
  def     lowercase		(    self			:		Tuple					)							->						Dict:
     __lowerCAmelCase							     =     DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge'					)

     __lowerCAmelCase							     =     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]]					)
     __lowerCAmelCase							     =     torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]					)
     with torch.no_grad():
        __lowerCAmelCase							     =     model(lowerCAmelCase_ ,  attention_mask=lowerCAmelCase_					)[0]
     # compare the actual values for a slice.
     __lowerCAmelCase							     =     torch.tensor(
         [[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]]					)
     self.assertTrue(torch.allclose(output[:, 1:4, 1:4] ,  lowerCAmelCase_ ,  atol=1e-4					) ,  f"""{output[:, 1:4, 1:4]}"""					)








from math import factorial



class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		Dict ,  lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		str					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     real
     if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     [1] * rank
     else:
        __lowerCAmelCase							     =     rank






  def __repr__(    self			:		Optional[Any]					)							->						Tuple:
     return (
         f"""{self.real}+"""
         f"""{"+".join(str(lowerCAmelCase_					)+"E"+str(n+1					)for n,dual in enumerate(self.duals					)					)}"""
     )






  def     lowercase		(    self			:		str					)							->						Dict:
     __lowerCAmelCase							     =     self.duals.copy()
     while cur[-1] == 0:
        cur.pop(-1					)
     return Dual(self.real ,  lowerCAmelCase_					)






  def __add__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		Tuple					)							->						Optional[Any]:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        return Dual(self.real + other ,  self.duals					)
     __lowerCAmelCase							     =     self.duals.copy()
     __lowerCAmelCase							     =     other.duals.copy()
     if len(lowerCAmelCase_					) > len(lowerCAmelCase_					):
        o_dual.extend([1] * (len(lowerCAmelCase_					) - len(lowerCAmelCase_					))					)
     elif len(lowerCAmelCase_					) < len(lowerCAmelCase_					):
        s_dual.extend([1] * (len(lowerCAmelCase_					) - len(lowerCAmelCase_					))					)
     __lowerCAmelCase							     =     []
     for i in range(len(lowerCAmelCase_					)					):
        new_duals.append(s_dual[i] + o_dual[i]					)
     return Dual(self.real + other.real ,  lowerCAmelCase_					)

  a_      =			__add__






  def __sub__(    self			:		int ,  lowerCAmelCase_			:		Dict					)							->						Optional[Any]:
     return self + other * -1






  def __mul__(    self			:		int ,  lowerCAmelCase_			:		Optional[int]					)							->						Dict:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i * other					)
        return Dual(self.real * other ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     [0] * (len(self.duals					) + len(other.duals					) + 1)
     for i, item in enumerate(self.duals					):
        for j, jtem in enumerate(other.duals					):
           new_duals[i + j + 1] += item * jtem
     for k in range(len(self.duals					)					):
        new_duals[k] += self.duals[k] * other.real
     for index in range(len(other.duals					)					):
        new_duals[index] += other.duals[index] * self.real
     return Dual(self.real * other.real ,  lowerCAmelCase_					)

  a_      =			__mul__






  def __truediv__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		str					)							->						Dict:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i / other					)
        return Dual(self.real / other ,  lowerCAmelCase_					)
     raise ValueError






  def __floordiv__(    self			:		str ,  lowerCAmelCase_			:		List[Any]					)							->						Union[str, Any]:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i // other					)
        return Dual(self.real // other ,  lowerCAmelCase_					)
     raise ValueError






  def __pow__(    self			:		Tuple ,  lowerCAmelCase_			:		Dict					)							->						List[str]:
     if n < 0 or isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        raise ValueError('power must be a positive integer'					)
     if n == 0:
        return 1
     if n == 1:
        return self
     __lowerCAmelCase							     =     self
     for _ in range(n - 1					):
        x *= self
     return x





def   a_   (			lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      Optional[Any]     ):
   if not callable(lowerCAmelCase_     ):
      raise ValueError('differentiate() requires a function as input for func'     )
   if not isinstance(lowerCAmelCase_,     (float, int)     ):
      raise ValueError('differentiate() requires a float as input for position'     )
   if not isinstance(lowerCAmelCase_,     lowerCAmelCase_     ):
      raise ValueError('differentiate() requires an int as input for order'     )
   __lowerCAmelCase							     =     Dual(lowerCAmelCase_,     1     )
   __lowerCAmelCase							     =     func(lowerCAmelCase_     )
   if order == 0:
      return result.real
   return result.duals[order - 1] * factorial(lowerCAmelCase_     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     def   a_   (			lowerCAmelCase_				:      int     ):
        return y**2 * y**4




     print(differentiate(f, 9, 2))

