





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        __SCREAMING_SNAKE_CASE      (			A_ ):







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







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Dict:
          lowerCamelCase__       :						Tuple        =							ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size      )

          lowerCamelCase__       :						Optional[Any]        =							None
          if self.use_input_mask:
             lowerCamelCase__       :						List[Any]        =							ids_tensor([self.batch_size, self.seq_length]		, vocab_size=2      )

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

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

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

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







       def 						__lowerCamelCase   (				self					:     Dict      )       ->Tuple:
          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 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->str:
          lowerCamelCase__       :						List[Any]        =							self.get_config()
          lowerCamelCase__       :						List[Any]        =							3_0_0
          return config







       def 						__lowerCamelCase   (				self					:     int		, A					:     int      )       ->Dict:
          self.parent.assertListEqual(list(result.loss.size()      )		, []      )







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     Dict		, A					:     List[str]		, A					:     Optional[Any]		, A					:     Tuple		, A					:     Tuple		, A					:     Optional[int]		, A					:     Tuple      )       ->Any:
          lowerCamelCase__       :						Optional[Any]        =							DebertaModel(config=_lowerCamelCase      )
          model.to(_lowerCamelCase      )
          model.eval()
          lowerCamelCase__       :						str        =							model(_lowerCamelCase		, attention_mask=_lowerCamelCase		, token_type_ids=_lowerCamelCase      )[0]
          lowerCamelCase__       :						Tuple        =							model(_lowerCamelCase		, token_type_ids=_lowerCamelCase      )[0]
          lowerCamelCase__       :						Union[str, Any]        =							model(_lowerCamelCase      )[0]

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







       def 						__lowerCamelCase   (				self					:     str		, A					:     List[str]		, A					:     str		, A					:     Dict		, A					:     Tuple		, A					:     Tuple		, A					:     Optional[int]		, A					:     int      )       ->Optional[Any]:
          lowerCamelCase__       :						List[str]        =							DebertaForMaskedLM(config=_lowerCamelCase      )
          model.to(_lowerCamelCase      )
          model.eval()
          lowerCamelCase__       :						Union[str, Any]        =							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 						__lowerCamelCase   (				self					:     Tuple		, A					:     Dict		, A					:     int		, A					:     Any		, A					:     List[Any]		, A					:     List[Any]		, A					:     List[Any]		, A					:     int      )       ->Tuple:
          lowerCamelCase__       :						str        =							self.num_labels
          lowerCamelCase__       :						str        =							DebertaForSequenceClassification(_lowerCamelCase      )
          model.to(_lowerCamelCase      )
          model.eval()
          lowerCamelCase__       :						Optional[int]        =							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 						__lowerCamelCase   (				self					:     Dict		, A					:     Dict		, A					:     Optional[int]		, A					:     Any		, A					:     Optional[Any]		, A					:     List[Any]		, A					:     str		, A					:     Optional[int]      )       ->Any:
          lowerCamelCase__       :						Optional[Any]        =							self.num_labels
          lowerCamelCase__       :						Optional[int]        =							DebertaForTokenClassification(config=_lowerCamelCase      )
          model.to(_lowerCamelCase      )
          model.eval()
          lowerCamelCase__       :						Union[str, Any]        =							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 						__lowerCamelCase   (				self					:     Tuple		, A					:     int		, A					:     List[Any]		, A					:     int		, A					:     str		, A					:     Tuple		, A					:     Optional[int]		, A					:     str      )       ->Tuple:
          lowerCamelCase__       :						Tuple        =							DebertaForQuestionAnswering(config=_lowerCamelCase      )
          model.to(_lowerCamelCase      )
          model.eval()
          lowerCamelCase__       :						Tuple        =							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 						__lowerCamelCase   (				self					:     Tuple      )       ->Optional[int]:
          lowerCamelCase__       :						int        =							self.prepare_config_and_inputs()
          (
              (
              lowerCamelCase__ 
          )       ,							(
              lowerCamelCase__ 
          )       ,							(
              lowerCamelCase__ 
          )       ,							(
              lowerCamelCase__ 
          )       ,							(
              lowerCamelCase__ 
          )       ,							(
              lowerCamelCase__ 
          )       ,							(
              lowerCamelCase__ 
          )       ,							 
          )       :						Dict        =							config_and_inputs
          lowerCamelCase__       :						Optional[Any]        =							{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
          return config, inputs_dict


@require_torch
class        __SCREAMING_SNAKE_CASE      (			A_					,A_					,unittest.TestCase ):
       _UpperCAmelCase				:  Tuple			   =   (
           (
               DebertaModel,
               DebertaForMaskedLM,
               DebertaForSequenceClassification,
               DebertaForTokenClassification,
               DebertaForQuestionAnswering,
           )
           if is_torch_available()
           else ()
       )
       _UpperCAmelCase				:  Optional[Any]			   =   (
           {
               "feature-extraction": DebertaModel,
               "fill-mask": DebertaForMaskedLM,
               "question-answering": DebertaForQuestionAnswering,
               "text-classification": DebertaForSequenceClassification,
               "token-classification": DebertaForTokenClassification,
               "zero-shot": DebertaForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )

       _UpperCAmelCase				:  str			   =   True
       _UpperCAmelCase				:  Optional[Any]			   =   False
       _UpperCAmelCase				:  Dict			   =   False
       _UpperCAmelCase				:  Any			   =   False
       _UpperCAmelCase				:  List[str]			   =   False







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Tuple:
          lowerCamelCase__       :						str        =							DebertaModelTester(self      )
          lowerCamelCase__       :						Optional[int]        =							ConfigTester(self		, config_class=_lowerCamelCase		, hidden_size=3_7      )







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->List[Any]:
          self.config_tester.run_common_tests()







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Optional[int]:
          lowerCamelCase__       :						Optional[int]        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_model(*_lowerCamelCase      )







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->List[str]:
          lowerCamelCase__       :						Dict        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_sequence_classification(*_lowerCamelCase      )







       def 						__lowerCamelCase   (				self					:     Dict      )       ->Any:
          lowerCamelCase__       :						List[Any]        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_masked_lm(*_lowerCamelCase      )







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->List[str]:
          lowerCamelCase__       :						List[Any]        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_question_answering(*_lowerCamelCase      )







       def 						__lowerCamelCase   (				self					:     str      )       ->List[Any]:
          lowerCamelCase__       :						List[Any]        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_token_classification(*_lowerCamelCase      )







       @slow
       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->List[str]:
          for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowerCamelCase__       :						str        =							DebertaModel.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 						__lowerCamelCase   (				self					:     List[Any]      )       ->Dict:
          pass







       @slow
       def 						__lowerCamelCase   (				self					:     int      )       ->Optional[int]:
          lowerCamelCase__       :						Optional[int]        =							DebertaModel.from_pretrained('''microsoft/deberta-base'''      )

          lowerCamelCase__       :						int        =							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__       :						Any        =							torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
          with torch.no_grad():
             lowerCamelCase__       :						str        =							model(_lowerCamelCase		, attention_mask=_lowerCamelCase      )[0]
          # compare the actual values for a slice.
          lowerCamelCase__       :						int        =							torch.tensor(
              [[[-0.59_86, -0.80_55, -0.84_62], [1.44_84, -0.93_48, -0.80_59], [0.31_23, 0.00_32, -1.41_31]]]      )
          self.assertTrue(torch.allclose(output[:, 1:4, 1:4]		, _lowerCamelCase		, atol=1e-4      )		, F"{output[:, 1:4, 1:4]}"      )











from math import factorial


class        __SCREAMING_SNAKE_CASE      :







       def __init__(				self					:     List[Any]		, A					:     Dict		, A					:     Any      )       ->Optional[Any]:
          lowerCamelCase__       :						Tuple        =							real
          if isinstance(A		, A      ):
             lowerCamelCase__       :						Optional[int]        =							[1] * rank
          else:
             lowerCamelCase__       :						List[Any]        =							rank







       def __repr__(				self					:     Tuple      )       ->str:
          return (
              F"{self.real}+"
              F"{'+'.join(str(A      )+'E'+str(n+1      )for n,dual in enumerate(self.duals      )      )}"
          )







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->List[Any]:
          lowerCamelCase__       :						Tuple        =							self.duals.copy()
          while cur[-1] == 0:
             cur.pop(-1      )
          return Dual(self.real		, A      )







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

       _UpperCAmelCase				:  List[Any]			   =   __add__







       def __sub__(				self					:     Any		, A					:     Dict      )       ->int:
          return self + other * -1







       def __mul__(				self					:     Optional[Any]		, A					:     List[Any]      )       ->Union[str, Any]:
          if not isinstance(A		, A      ):
             lowerCamelCase__       :						Optional[int]        =							[]
             for i in self.duals:
                new_duals.append(i * other      )
             return Dual(self.real * other		, A      )
          lowerCamelCase__       :						Tuple        =							[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		, A      )

       _UpperCAmelCase				:  Optional[Any]			   =   __mul__







       def __truediv__(				self					:     int		, A					:     List[Any]      )       ->Dict:
          if not isinstance(A		, A      ):
             lowerCamelCase__       :						Tuple        =							[]
             for i in self.duals:
                new_duals.append(i / other      )
             return Dual(self.real / other		, A      )
          raise ValueError







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







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



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    Dict:







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


if __name__ == "__main__":
       import doctest

       doctest.testmod()



       def 	_a						(   UpperCAmelCase					)    ->    int:







          """simple docstring"""
          return y**2 * y**4




       print(differentiate(f, 9, 2))



