



"""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			(   lowerCAmelCase_     ):


					'''simple docstring'''





					def __init__( self							:						Any,						lowerCamelCase							:						Optional[Any],						lowerCamelCase							:						Optional[int]=13,						lowerCamelCase							:						str=7,						lowerCamelCase							:						Dict=True,						lowerCamelCase							:						int=True,						lowerCamelCase							:						Dict=True,						lowerCamelCase							:						int=True,						lowerCamelCase							:						Optional[int]=99,						lowerCamelCase							:						Dict=32,						lowerCamelCase							:						Tuple=5,						lowerCamelCase							:						List[Any]=4,						lowerCamelCase							:						int=37,						lowerCamelCase							:						Optional[Any]="gelu",						lowerCamelCase							:						int=0.1,						lowerCamelCase							:						Any=0.1,						lowerCamelCase							:						Dict=512,						lowerCamelCase							:						str=16,						lowerCamelCase							:						Dict=2,						lowerCamelCase							:						Union[str, Any]=0.02,						lowerCamelCase							:						Tuple=False,						lowerCamelCase							:						Dict=True,						lowerCamelCase							:						Optional[int]="None",						lowerCamelCase							:						Union[str, Any]=3,						lowerCamelCase							:						Optional[int]=4,						lowerCamelCase							:						Optional[int]=None,						)->      List[Any]:
										lowerCamelCase__	:	Union[str, Any]				=parent
										lowerCamelCase__	:	Union[str, Any]				=batch_size
										lowerCamelCase__	:	Optional[int]				=seq_length
										lowerCamelCase__	:	int				=is_training
										lowerCamelCase__	:	str				=use_input_mask
										lowerCamelCase__	:	int				=use_token_type_ids
										lowerCamelCase__	:	Optional[int]				=use_labels
										lowerCamelCase__	:	Optional[Any]				=vocab_size
										lowerCamelCase__	:	Tuple				=hidden_size
										lowerCamelCase__	:	Any				=num_hidden_layers
										lowerCamelCase__	:	int				=num_attention_heads
										lowerCamelCase__	:	List[str]				=intermediate_size
										lowerCamelCase__	:	List[str]				=hidden_act
										lowerCamelCase__	:	Any				=hidden_dropout_prob
										lowerCamelCase__	:	int				=attention_probs_dropout_prob
										lowerCamelCase__	:	str				=max_position_embeddings
										lowerCamelCase__	:	str				=type_vocab_size
										lowerCamelCase__	:	List[str]				=type_sequence_label_size
										lowerCamelCase__	:	List[Any]				=initializer_range
										lowerCamelCase__	:	List[Any]				=num_labels
										lowerCamelCase__	:	List[Any]				=num_choices
										lowerCamelCase__	:	List[str]				=relative_attention
										lowerCamelCase__	:	str				=position_biased_input
										lowerCamelCase__	:	Optional[int]				=pos_att_type
										lowerCamelCase__	:	str				=scope



					def 	snake_case ( self							:						str  )->      int:
										lowerCamelCase__	:	Union[str, Any]				=ids_tensor([self.batch_size, self.seq_length],						self.vocab_size  )

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

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

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

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

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



					def 	snake_case ( self							:						Any  )->      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 	snake_case ( self							:						int,						lowerCamelCase							:						Dict  )->      Optional[int]:
										self.parent.assertListEqual(list(result.loss.size()  ),						[]  )



					def 	snake_case ( self							:						Any,						lowerCamelCase							:						int,						lowerCamelCase							:						str,						lowerCamelCase							:						Any,						lowerCamelCase							:						int,						lowerCamelCase							:						Dict,						lowerCamelCase							:						Tuple,						lowerCamelCase							:						Any  )->      List[str]:
										lowerCamelCase__	:	Dict				=DebertaVaModel(config=lowerCamelCase  )
										model.to(lowerCamelCase  )
										model.eval()
										lowerCamelCase__	:	List[str]				=model(lowerCamelCase,						attention_mask=lowerCamelCase,						token_type_ids=lowerCamelCase  )[0]
										lowerCamelCase__	:	int				=model(lowerCamelCase,						token_type_ids=lowerCamelCase  )[0]
										lowerCamelCase__	:	List[str]				=model(lowerCamelCase  )[0]

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



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




					def 	snake_case ( self							:						Any  )->      List[Any]:
										lowerCamelCase__	:	str				=self.prepare_config_and_inputs()
										(
										    lowerCamelCase__     
										)	:	Optional[int]				=config_and_inputs
										lowerCamelCase__	:	List[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			(   lowerCAmelCase_	,     lowerCAmelCase_	,     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 	snake_case ( self							:						List[Any]  )->      int:
										lowerCamelCase__	:	Optional[int]				=DebertaVaModelTester(self  )
										lowerCamelCase__	:	Any				=ConfigTester(self,						config_class=lowerCamelCase,						hidden_size=37  )



					def 	snake_case ( self							:						Any  )->      Dict:
										self.config_tester.run_common_tests()



					def 	snake_case ( self							:						List[str]  )->      int:
										lowerCamelCase__	:	Dict				=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_deberta_model(*lowerCamelCase  )



					def 	snake_case ( self							:						Optional[Any]  )->      Any:
										lowerCamelCase__	:	str				=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCamelCase  )



					def 	snake_case ( self							:						Dict  )->      Optional[int]:
										lowerCamelCase__	:	Optional[Any]				=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCamelCase  )



					def 	snake_case ( self							:						Any  )->      Dict:
										lowerCamelCase__	:	Optional[int]				=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_deberta_for_question_answering(*lowerCamelCase  )



					def 	snake_case ( self							:						Optional[int]  )->      str:
										lowerCamelCase__	:	List[Any]				=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_deberta_for_token_classification(*lowerCamelCase  )



					def 	snake_case ( self							:						str  )->      str:
										lowerCamelCase__	:	str				=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_deberta_for_multiple_choice(*lowerCamelCase  )




					@slow
					def 	snake_case ( self							:						List[str]  )->      Optional[int]:
										for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															lowerCamelCase__	:	Optional[Any]				=DebertaVaModel.from_pretrained(lowerCamelCase  )
															self.assertIsNotNone(lowerCamelCase  )





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


					'''simple docstring'''





					@unittest.skip(reason='''Model not available yet'''  )
					def 	snake_case ( self							:						Optional[Any]  )->      Optional[Any]:
										pass




					@slow
					def 	snake_case ( self							:						Dict  )->      Any:
										lowerCamelCase__	:	List[Any]				=DebertaVaModel.from_pretrained('''microsoft/deberta-v2-xlarge'''  )

										lowerCamelCase__	:	Optional[int]				=torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]  )
										lowerCamelCase__	:	Optional[int]				=torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
										with torch.no_grad():
															lowerCamelCase__	:	Union[str, Any]				=model(lowerCamelCase,						attention_mask=lowerCamelCase  )[0]
										# compare the actual values for a slice.
										lowerCamelCase__	:	str				=torch.tensor(
										    [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]]  )
										self.assertTrue(torch.allclose(output[:, 1:4, 1:4],						lowerCamelCase,						atol=1E-4  ),						F'''{output[:, 1:4, 1:4]}'''  )









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					list							,	__lowerCamelCase       :					list							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					int			):
			"""simple docstring"""
			if index == number_of_items:
						return 0
			lowerCamelCase__	:	Optional[int]				=0
			lowerCamelCase__	:	Union[str, Any]				=0
			lowerCamelCase__	:	List[str]				=knapsack(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	index + 1			)
			if weights[index] <= max_weight:
						lowerCamelCase__	:	Dict				=values[index] + knapsack(
						    __lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	max_weight - weights[index]							,	index + 1			)
			return max(__lowerCamelCase							,	__lowerCamelCase			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




