
'''simple docstring'''
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  __A				( _SCREAMING_SNAKE_CASE			):
 def __init__(   self			:    Dict				,   lowerCamelCase			:    Union[str, Any]				,   lowerCamelCase			:    Union[str, Any]=13				,   lowerCamelCase			:    List[Any]=7				,   lowerCamelCase			:    Tuple=True				,   lowerCamelCase			:    Union[str, Any]=True				,   lowerCamelCase			:    Tuple=True				,   lowerCamelCase			:    str=True				,   lowerCamelCase			:    Optional[Any]=99				,   lowerCamelCase			:    Dict=32				,   lowerCamelCase			:    Union[str, Any]=5				,   lowerCamelCase			:    Union[str, Any]=4				,   lowerCamelCase			:    Tuple=37				,   lowerCamelCase			:    Optional[Any]="gelu"				,   lowerCamelCase			:    List[str]=0.1				,   lowerCamelCase			:    List[Any]=0.1				,   lowerCamelCase			:    Union[str, Any]=5_12				,   lowerCamelCase			:    str=16				,   lowerCamelCase			:    Optional[int]=2				,   lowerCamelCase			:    Tuple=0.02				,   lowerCamelCase			:    str=False				,   lowerCamelCase			:    Dict=True				,   lowerCamelCase			:    Union[str, Any]="None"				,   lowerCamelCase			:    str=3				,   lowerCamelCase			:    Optional[Any]=4				,   lowerCamelCase			:    int=None				,   ):



   """simple docstring"""





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



   """simple docstring"""





   __A		:     List[Any]												=		ids_tensor([self.batch_size, self.seq_length]				,   self.vocab_size       )

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

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

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

   __A		:     Optional[int]												=		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 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 		lowercase_(   self			:    Optional[int]       ):



   """simple docstring"""





   __A		:     Optional[Any]												=		self.get_config()
   __A		:     Any												=		3_00
   return config
 def 		lowercase_(   self			:    str				,   lowerCamelCase			:    Optional[int]       ):



   """simple docstring"""





   self.parent.assertListEqual(list(result.loss.size()       )				,   []       )
 def 		lowercase_(   self			:    Union[str, Any]				,   lowerCamelCase			:    str				,   lowerCamelCase			:    str				,   lowerCamelCase			:    str				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    int				,   lowerCamelCase			:    Dict				,   lowerCamelCase			:    Any       ):



   """simple docstring"""





   __A		:     int												=		DebertaModel(config=lowerCamelCase       )
   model.to(lowerCamelCase       )
   model.eval()
   __A		:     Any												=		model(lowerCamelCase				,   attention_mask=lowerCamelCase				,   token_type_ids=lowerCamelCase       )[0]
   __A		:     Optional[Any]												=		model(lowerCamelCase				,   token_type_ids=lowerCamelCase       )[0]
   __A		:     List[str]												=		model(lowerCamelCase       )[0]

   self.parent.assertListEqual(list(sequence_output.size()       )				,   [self.batch_size, self.seq_length, self.hidden_size]       )
 def 		lowercase_(   self			:    Union[str, Any]				,   lowerCamelCase			:    List[Any]				,   lowerCamelCase			:    List[Any]				,   lowerCamelCase			:    List[str]				,   lowerCamelCase			:    List[Any]				,   lowerCamelCase			:    List[str]				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    Optional[Any]       ):



   """simple docstring"""





   __A		:     int												=		DebertaForMaskedLM(config=lowerCamelCase       )
   model.to(lowerCamelCase       )
   model.eval()
   __A		:     Dict												=		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			:    Tuple				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    str				,   lowerCamelCase			:    Dict				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    Dict				,   lowerCamelCase			:    Tuple				,   lowerCamelCase			:    List[Any]       ):



   """simple docstring"""





   __A		:     str												=		self.num_labels
   __A		:     Tuple												=		DebertaForSequenceClassification(lowerCamelCase       )
   model.to(lowerCamelCase       )
   model.eval()
   __A		:     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 		lowercase_(   self			:    Union[str, Any]				,   lowerCamelCase			:    Optional[int]				,   lowerCamelCase			:    Dict				,   lowerCamelCase			:    List[Any]				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    Dict				,   lowerCamelCase			:    List[Any]       ):



   """simple docstring"""





   __A		:     Optional[int]												=		self.num_labels
   __A		:     Optional[Any]												=		DebertaForTokenClassification(config=lowerCamelCase       )
   model.to(lowerCamelCase       )
   model.eval()
   __A		:     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 		lowercase_(   self			:    int				,   lowerCamelCase			:    Union[str, Any]				,   lowerCamelCase			:    str				,   lowerCamelCase			:    Union[str, Any]				,   lowerCamelCase			:    List[str]				,   lowerCamelCase			:    List[str]				,   lowerCamelCase			:    str				,   lowerCamelCase			:    Optional[Any]       ):



   """simple docstring"""





   __A		:     List[Any]												=		DebertaForQuestionAnswering(config=lowerCamelCase       )
   model.to(lowerCamelCase       )
   model.eval()
   __A		:     Optional[Any]												=		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			:    Optional[Any]       ):



   """simple docstring"""





   __A		:     str												=		self.prepare_config_and_inputs()
   (
       (
       __A
   )							,					(
       __A
   )							,					(
       __A
   )							,					(
       __A
   )							,					(
       __A
   )							,					(
       __A
   )							,					(
       __A
   )							,					
   )		:     List[Any]												=		config_and_inputs
   __A		:     str												=		{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
   return config, inputs_dict







@require_torch
class  __A				( _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						unittest.TestCase			):
 lowerCamelCase				       =(
     (
         DebertaModel,
         DebertaForMaskedLM,
         DebertaForSequenceClassification,
         DebertaForTokenClassification,
         DebertaForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 lowerCamelCase				       =(
     {
         '''feature-extraction''': DebertaModel,
         '''fill-mask''': DebertaForMaskedLM,
         '''question-answering''': DebertaForQuestionAnswering,
         '''text-classification''': DebertaForSequenceClassification,
         '''token-classification''': DebertaForTokenClassification,
         '''zero-shot''': DebertaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )

 lowerCamelCase				       =True
 lowerCamelCase				       =False
 lowerCamelCase				       =False
 lowerCamelCase				       =False
 lowerCamelCase				       =False
 def 		lowercase_(   self			:    Dict       ):



   """simple docstring"""





   __A		:     List[Any]												=		DebertaModelTester(self       )
   __A		:     List[Any]												=		ConfigTester(self				,   config_class=lowerCamelCase				,   hidden_size=37       )
 def 		lowercase_(   self			:    Optional[int]       ):



   """simple docstring"""





   self.config_tester.run_common_tests()
 def 		lowercase_(   self			:    List[Any]       ):



   """simple docstring"""





   __A		:     Union[str, Any]												=		self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_deberta_model(*lowerCamelCase       )
 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(*lowerCamelCase       )
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   __A		:     Tuple												=		self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCamelCase       )
 def 		lowercase_(   self			:    int       ):



   """simple docstring"""





   __A		:     Union[str, Any]												=		self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_deberta_for_question_answering(*lowerCamelCase       )
 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   __A		:     Optional[Any]												=		self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_deberta_for_token_classification(*lowerCamelCase       )






 @slow
 def 		lowercase_(   self			:    Tuple       ):



   """simple docstring"""





   for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     __A		:     List[Any]												=		DebertaModel.from_pretrained(lowerCamelCase       )
     self.assertIsNotNone(lowerCamelCase       )







@require_torch
@require_sentencepiece
@require_tokenizers
class  __A				( unittest.TestCase			):
 @unittest.skip(reason="""Model not available yet"""       )
 def 		lowercase_(   self			:    List[Any]       ):



   """simple docstring"""





   pass






 @slow
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   __A		:     int												=		DebertaModel.from_pretrained("""microsoft/deberta-base"""       )

   __A		:     Optional[int]												=		torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]       )
   __A		:     str												=		torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
   with torch.no_grad():
     __A		:     List[Any]												=		model(lowerCamelCase				,   attention_mask=lowerCamelCase       )[0]
   # compare the actual values for a slice.
   __A		:     Union[str, Any]												=		torch.tensor(
       [[[-0.5_986, -0.8_055, -0.8_462], [1.4_484, -0.9_348, -0.8_059], [0.3_123, 0.0_032, -1.4_131]]]       )
   self.assertTrue(torch.allclose(output[:, 1:4, 1:4]				,   lowerCamelCase				,   atol=1e-4       )				,   f"{output[:, 1:4, 1:4]}"       )





'''simple docstring'''
from __future__ import annotations







def        A_     (						__SCREAMING_SNAKE_CASE						:  list[int] )				->       int:





  """simple docstring"""

  if not nums:
    return 0
  __A		:     List[Any]												=		nums[0]
  __A		:     Union[str, Any]												=		0
  for num in nums[1:]:
    __A							,					__A		:     Union[str, Any]												=		(
        max_excluding + num,
        max(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ),
    )
  return max(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
     import doctest

     doctest.testmod()



