

"""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 lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




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




    def UpperCamelCase__		( self    )    ->      Optional[int]:
          A				  =		ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size    )

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

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

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

          A				  =		self.get_config()

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




    def UpperCamelCase__		( self    )    ->      List[str]:
          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 UpperCamelCase__		( self ,lowerCamelCase_    )    ->      str:
          self.parent.assertListEqual(list(result.loss.size()    ) ,[]    )




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    )    ->      Tuple:
          A				  =		DebertaVaModel(config=lowerCamelCase_    )
          model.to(lowerCamelCase_    )
          model.eval()
          A				  =		model(lowerCamelCase_ ,attention_mask=lowerCamelCase_ ,token_type_ids=lowerCamelCase_    )[0]
          A				  =		model(lowerCamelCase_ ,token_type_ids=lowerCamelCase_    )[0]
          A				  =		model(lowerCamelCase_    )[0]

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




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




    def UpperCamelCase__		( self    )    ->      Optional[Any]:
          A				  =		self.prepare_config_and_inputs()
          (
              (
              A
          )  ,			(
              A
          )  ,			(
              A
          )  ,			(
              A
          )  ,			(
              A
          )  ,			(
              A
          )  ,			(
              A
          )  ,			
          )				  =		config_and_inputs
          A				  =		{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
          return config, inputs_dict





@require_torch
class lowerCamelCase__   (				SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   unittest.TestCase    ):



    '''simple docstring'''




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

    _lowerCamelCase					=   True
    _lowerCamelCase					=   False
    _lowerCamelCase					=   False
    _lowerCamelCase					=   False
    _lowerCamelCase					=   False




    def UpperCamelCase__		( self    )    ->      Optional[Any]:
          A				  =		DebertaVaModelTester(self    )
          A				  =		ConfigTester(self ,config_class=lowerCamelCase_ ,hidden_size=3_7    )




    def UpperCamelCase__		( self    )    ->      Optional[int]:
          self.config_tester.run_common_tests()




    def UpperCamelCase__		( self    )    ->      Tuple:
          A				  =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_model(*lowerCamelCase_    )




    def UpperCamelCase__		( self    )    ->      int:
          A				  =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCamelCase_    )




    def UpperCamelCase__		( self    )    ->      Tuple:
          A				  =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCamelCase_    )




    def UpperCamelCase__		( self    )    ->      Any:
          A				  =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_question_answering(*lowerCamelCase_    )




    def UpperCamelCase__		( self    )    ->      List[Any]:
          A				  =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_token_classification(*lowerCamelCase_    )




    def UpperCamelCase__		( self    )    ->      Tuple:
          A				  =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_multiple_choice(*lowerCamelCase_    )




    @slow
    def UpperCamelCase__		( self    )    ->      Any:
          for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                A				  =		DebertaVaModel.from_pretrained(lowerCamelCase_    )
                self.assertIsNotNone(lowerCamelCase_    )





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



    '''simple docstring'''




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




    @slow
    def UpperCamelCase__		( self    )    ->      Optional[Any]:
          A				  =		DebertaVaModel.from_pretrained("""microsoft/deberta-v2-xlarge"""    )

          A				  =		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]]    )
          A				  =		torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
          with torch.no_grad():
                A				  =		model(lowerCamelCase_ ,attention_mask=lowerCamelCase_    )[0]
          # compare the actual values for a slice.
          A				  =		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]}'    )







"""simple docstring"""


import unittest

from knapsack import greedy_knapsack as kp





class lowerCamelCase__   (				unittest.TestCase    ):



    '''simple docstring'''




    def UpperCamelCase__		( self    )    ->      Any:
          A				  =		[1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
          A				  =		[2, 4, 6, 8, 1_0, 1_2]
          A				  =		1_0_0
          self.assertEqual(kp.calc_profit(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    ) ,2_1_0    )




    def UpperCamelCase__		( self    )    ->      List[Any]:
          self.assertRaisesRegex(lowerCamelCase_ ,"""max_weight must greater than zero."""    )




    def UpperCamelCase__		( self    )    ->      Any:
          self.assertRaisesRegex(lowerCamelCase_ ,"""Weight can not be negative."""    )




    def UpperCamelCase__		( self    )    ->      str:
          self.assertRaisesRegex(lowerCamelCase_ ,"""Profit can not be negative."""    )




    def UpperCamelCase__		( self    )    ->      Optional[int]:
          self.assertRaisesRegex(lowerCamelCase_ ,"""max_weight must greater than zero."""    )




    def UpperCamelCase__		( self    )    ->      str:
          self.assertRaisesRegex(
              lowerCamelCase_ ,"""The length of profit and weight must be same."""    )


if __name__ == "__main__":
  unittest.main()




