"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TFDebertaVaForMaskedLM,
        TFDebertaVaForQuestionAnswering,
        TFDebertaVaForSequenceClassification,
        TFDebertaVaForTokenClassification,
        TFDebertaVaModel,
    )


class        a				:


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


    def      lowerCAmelCase_  (					self	:  str					):
      _UpperCAmelCase						 =      ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size					)

      _UpperCAmelCase						 =      None
      if self.use_input_mask:
        _UpperCAmelCase						 =      random_attention_mask([self.batch_size, self.seq_length]					)

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

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

      _UpperCAmelCase						 =      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   ,				relative_attention=self.relative_attention   ,				position_biased_input=self.position_biased_input   ,				initializer_range=self.initializer_range   ,				return_dict=__lowerCAmelCase   ,				)

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


    def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  Dict					):
      _UpperCAmelCase						 =      TFDebertaVaModel(config=__lowerCAmelCase					)
      _UpperCAmelCase						 =      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

      _UpperCAmelCase						 =      [input_ids, input_mask]
      _UpperCAmelCase						 =      model(__lowerCAmelCase					)

      _UpperCAmelCase						 =      model(__lowerCAmelCase					)

      self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)					)


    def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  List[str]					):
      _UpperCAmelCase						 =      TFDebertaVaForMaskedLM(config=__lowerCAmelCase					)
      _UpperCAmelCase						 =      {
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }
      _UpperCAmelCase						 =      model(__lowerCAmelCase					)
      self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.vocab_size)					)


    def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  Optional[Any]					):
      _UpperCAmelCase						 =      self.num_labels
      _UpperCAmelCase						 =      TFDebertaVaForSequenceClassification(config=__lowerCAmelCase					)
      _UpperCAmelCase						 =      {
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }

      _UpperCAmelCase						 =      model(__lowerCAmelCase					)
      self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)					)


    def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  List[Any]					):
      _UpperCAmelCase						 =      self.num_labels
      _UpperCAmelCase						 =      TFDebertaVaForTokenClassification(config=__lowerCAmelCase					)
      _UpperCAmelCase						 =      {
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }
      _UpperCAmelCase						 =      model(__lowerCAmelCase					)
      self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.num_labels)					)


    def      lowerCAmelCase_  (					self	:  int   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  Tuple					):
      _UpperCAmelCase						 =      TFDebertaVaForQuestionAnswering(config=__lowerCAmelCase					)
      _UpperCAmelCase						 =      {
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }

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



@require_tf
class        a				(		lowerCAmelCase_	,							lowerCAmelCase_	,							unittest.TestCase   ):
    _snake_case     :  str     							=							(
        (
            TFDebertaVaModel,
            TFDebertaVaForMaskedLM,
            TFDebertaVaForQuestionAnswering,
            TFDebertaVaForSequenceClassification,
            TFDebertaVaForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    _snake_case     :  List[str]     							=							(
        {
            'feature-extraction': TFDebertaVaModel,
            'fill-mask': TFDebertaVaForMaskedLM,
            'question-answering': TFDebertaVaForQuestionAnswering,
            'text-classification': TFDebertaVaForSequenceClassification,
            'token-classification': TFDebertaVaForTokenClassification,
            'zero-shot': TFDebertaVaForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )

    _snake_case     :  Optional[Any]     							=							False
    _snake_case     :  Tuple     							=							False


    def      lowerCAmelCase_  (					self	:  int					):
      _UpperCAmelCase						 =      TFDebertaVaModelTester(self					)
      _UpperCAmelCase						 =      ConfigTester(self   ,				config_class=__lowerCAmelCase   ,				hidden_size=37					)


    def      lowerCAmelCase_  (					self	:  Any					):
      self.config_tester.run_common_tests()


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Tuple					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase					)


    @slow
    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      _UpperCAmelCase						 =      TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""					)
      self.assertIsNotNone(__lowerCAmelCase					)



@require_tf
class        a				(		unittest.TestCase   ):


    @unittest.skip(reason="""Model not available yet"""					)
    def      lowerCAmelCase_  (					self	:  Any					):
      pass


    @slow
    def      lowerCAmelCase_  (					self	:  Tuple					):
      _UpperCAmelCase						 =      TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""					)
      _UpperCAmelCase						 =      tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]					)
      _UpperCAmelCase						 =      tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]					)
      _UpperCAmelCase						 =      model(__lowerCAmelCase   ,				attention_mask=__lowerCAmelCase					)[0]

      _UpperCAmelCase						 =      tf.constant(
          [[[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]]]					)
      tf.debugging.assert_near(output[:, 1:4, 1:4]   ,				__lowerCAmelCase   ,				atol=1e-4					)

"""simple docstring"""


import unittest

from knapsack import knapsack as k


class        a				(		unittest.TestCase   ):


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      _UpperCAmelCase						 =      0
      _UpperCAmelCase						 =      [0]
      _UpperCAmelCase						 =      [0]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				0					)

      _UpperCAmelCase						 =      [60]
      _UpperCAmelCase						 =      [10]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				0					)


    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      _UpperCAmelCase						 =      3
      _UpperCAmelCase						 =      [1, 2, 3]
      _UpperCAmelCase						 =      [3, 2, 1]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				5					)


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      50
      _UpperCAmelCase						 =      [60, 100, 120]
      _UpperCAmelCase						 =      [10, 20, 30]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				220					)


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