




'''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    (						snake_case_				):






      def __init__( self   :  Tuple						,						lowercase__   :  Optional[int]						,						lowercase__   :  Optional[int]=13						,						lowercase__   :  str=7						,						lowercase__   :  int=True						,						lowercase__   :  Any=True						,						lowercase__   :  Union[str, Any]=True						,						lowercase__   :  List[str]=True						,						lowercase__   :  int=99						,						lowercase__   :  Union[str, Any]=32						,						lowercase__   :  Any=5						,						lowercase__   :  Dict=4						,						lowercase__   :  Optional[Any]=37						,						lowercase__   :  Optional[Any]="gelu"						,						lowercase__   :  str=0.1						,						lowercase__   :  Tuple=0.1						,						lowercase__   :  Any=512						,						lowercase__   :  int=16						,						lowercase__   :  Optional[Any]=2						,						lowercase__   :  Dict=0.02						,						lowercase__   :  Tuple=False						,						lowercase__   :  Dict=True						,						lowercase__   :  str="None"						,						lowercase__   :  List[Any]=3						,						lowercase__   :  List[Any]=4						,						lowercase__   :  List[str]=None						,						):


           '''simple docstring'''

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






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           a_     :							Tuple     						=  ids_tensor([self.batch_size, self.seq_length]						,						self.vocab_size       )

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

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

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

           a_     :							Tuple     						=  self.get_config()

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






      def 							lowercase_						( self   :  Optional[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 							lowercase_						( self   :  Any						,						lowercase__   :  int       ):


           '''simple docstring'''

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






      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  Tuple						,						lowercase__   :  Any						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  Tuple						,						lowercase__   :  Dict						,						lowercase__   :  List[str]						,						lowercase__   :  Any       ):


           '''simple docstring'''

           a_     :							Any     						=  DebertaVaModel(config=lowercase__       )
           model.to(lowercase__       )
           model.eval()
           a_     :							List[Any]     						=  model(lowercase__						,						attention_mask=lowercase__						,						token_type_ids=lowercase__       )[0]
           a_     :							Union[str, Any]     						=  model(lowercase__						,						token_type_ids=lowercase__       )[0]
           a_     :							Tuple     						=  model(lowercase__       )[0]

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






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


           '''simple docstring'''

           a_     :							Dict     						=  DebertaVaForMaskedLM(config=lowercase__       )
           model.to(lowercase__       )
           model.eval()
           a_     :							Union[str, Any]     						=  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 							lowercase_						( self   :  str						,						lowercase__   :  Any						,						lowercase__   :  Tuple						,						lowercase__   :  Any						,						lowercase__   :  int						,						lowercase__   :  Tuple						,						lowercase__   :  Optional[Any]						,						lowercase__   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							Dict     						=  self.num_labels
           a_     :							Union[str, Any]     						=  DebertaVaForSequenceClassification(lowercase__       )
           model.to(lowercase__       )
           model.eval()
           a_     :							Any     						=  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 							lowercase_						( self   :  Any						,						lowercase__   :  List[Any]						,						lowercase__   :  Dict						,						lowercase__   :  Optional[int]						,						lowercase__   :  List[Any]						,						lowercase__   :  List[str]						,						lowercase__   :  str						,						lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							Dict     						=  self.num_labels
           a_     :							Tuple     						=  DebertaVaForTokenClassification(config=lowercase__       )
           model.to(lowercase__       )
           model.eval()
           a_     :							int     						=  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 							lowercase_						( self   :  int						,						lowercase__   :  List[Any]						,						lowercase__   :  Optional[Any]						,						lowercase__   :  int						,						lowercase__   :  str						,						lowercase__   :  Tuple						,						lowercase__   :  int						,						lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							Tuple     						=  DebertaVaForQuestionAnswering(config=lowercase__       )
           model.to(lowercase__       )
           model.eval()
           a_     :							List[str]     						=  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 							lowercase_						( self   :  Tuple						,						lowercase__   :  Dict						,						lowercase__   :  Tuple						,						lowercase__   :  Dict						,						lowercase__   :  int						,						lowercase__   :  int						,						lowercase__   :  List[str]						,						lowercase__   :  Tuple       ):


           '''simple docstring'''

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






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


           '''simple docstring'''

           a_     :							Tuple     						=  self.prepare_config_and_inputs()
           (
               (
               a_
           )						,     (
               a_
           )						,     (
               a_
           )						,     (
               a_
           )						,     (
               a_
           )						,     (
               a_
           )						,     (
               a_
           )						,     
           )     :							Optional[int]     						=  config_and_inputs
           a_     :							List[str]     						=  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
           return config, inputs_dict







@require_torch
class      SCREAMING_SNAKE_CASE    (						snake_case_							,      snake_case_							,      unittest.TestCase				):
      __magic_name__  :       Dict   							=					(
          (
              DebertaVaModel,
              DebertaVaForMaskedLM,
              DebertaVaForSequenceClassification,
              DebertaVaForTokenClassification,
              DebertaVaForQuestionAnswering,
              DebertaVaForMultipleChoice,
          )
          if is_torch_available()
          else ()
      )
      __magic_name__  :       Tuple   							=					(
          {
              '''feature-extraction''': DebertaVaModel,
              '''fill-mask''': DebertaVaForMaskedLM,
              '''question-answering''': DebertaVaForQuestionAnswering,
              '''text-classification''': DebertaVaForSequenceClassification,
              '''token-classification''': DebertaVaForTokenClassification,
              '''zero-shot''': DebertaVaForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )

      __magic_name__  :       Any   							=					True
      __magic_name__  :       List[Any]   							=					False
      __magic_name__  :       Tuple   							=					False
      __magic_name__  :       str   							=					False
      __magic_name__  :       Any   							=					False






      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           a_     :							int     						=  DebertaVaModelTester(self       )
           a_     :							Union[str, Any]     						=  ConfigTester(self						,						config_class=lowercase__						,						hidden_size=37       )






      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           self.config_tester.run_common_tests()






      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							int     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_model(*lowercase__       )






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


           '''simple docstring'''

           a_     :							int     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_sequence_classification(*lowercase__       )






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							str     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_masked_lm(*lowercase__       )






      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           a_     :							Any     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_question_answering(*lowercase__       )






      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							List[str]     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_token_classification(*lowercase__       )






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           a_     :							Dict     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_multiple_choice(*lowercase__       )






      @slow
      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                a_     :							Optional[Any]     						=  DebertaVaModel.from_pretrained(lowercase__       )
                self.assertIsNotNone(lowercase__       )







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






      @unittest.skip(reason="""Model not available yet"""       )
      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           pass






      @slow
      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           a_     :							Any     						=  DebertaVaModel.from_pretrained("""microsoft/deberta-v2-xlarge"""       )

           a_     :							List[str]     						=  torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]       )
           a_     :							Tuple     						=  torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
           with torch.no_grad():
                a_     :							Dict     						=  model(lowercase__						,						attention_mask=lowercase__       )[0]
           # compare the actual values for a slice.
           a_     :							str     						=  torch.tensor(
               [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]       )
           self.assertTrue(torch.allclose(output[:, 1:4, 1:4]						,						lowercase__						,						atol=1e-4       )						,						F"{output[:, 1:4, 1:4]}"       )






'''simple docstring'''


import math
import random



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float ,    UpperCamelCase__    :					bool = False				):




     """simple docstring"""


     if deriv:
          return value * (1 - value)
     return 1 / (1 + math.exp(-value				))


# Initial Value
lowerCAmelCase_      :  Optional[Any]				     =  0.02



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					int ,    UpperCamelCase__    :					int				):




     """simple docstring"""


     a_     :							Tuple     						=  float(2 * (random.randint(1 ,    100				)) - 1				)

     for _ in range(UpperCamelCase__				):
          # Forward propagation
          a_     :							Dict     						=  sigmoid_function(INITIAL_VALUE * weight				)
          # How much did we miss?
          a_     :							Dict     						=  (expected / 100) - layer_a
          # Error delta
          a_     :							Optional[Any]     						=  layer_1_error * sigmoid_function(UpperCamelCase__ ,    UpperCamelCase__				)
          # Update weight
          weight += INITIAL_VALUE * layer_1_delta

     return layer_a * 100


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCAmelCase_      :  str				     =  int(input('Expected value: '))
 lowerCAmelCase_      :  str				     =  int(input('Number of propagations: '))
 print(forward_propagation(expected, number_propagations))
