





'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import DebertaConfig, 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 (
          TFDebertaForMaskedLM,
          TFDebertaForQuestionAnswering,
          TFDebertaForSequenceClassification,
          TFDebertaForTokenClassification,
          TFDebertaModel,
      )

class       lowerCamelCase_							:






   '''simple docstring'''

   def __init__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase=13     ,						__UpperCamelCase=7     ,						__UpperCamelCase=True     ,						__UpperCamelCase=True     ,						__UpperCamelCase=True     ,						__UpperCamelCase=True     ,						__UpperCamelCase=99     ,						__UpperCamelCase=32     ,						__UpperCamelCase=2     ,						__UpperCamelCase=4     ,						__UpperCamelCase=37     ,						__UpperCamelCase="gelu"     ,						__UpperCamelCase=0.1     ,						__UpperCamelCase=0.1     ,						__UpperCamelCase=5_12     ,						__UpperCamelCase=16     ,						__UpperCamelCase=2     ,						__UpperCamelCase=0.02     ,						__UpperCamelCase=3     ,						__UpperCamelCase=4     ,						__UpperCamelCase=None     ,						):
       UpperCamelCase__      =			parent
       UpperCamelCase__      =			13
       UpperCamelCase__      =			7
       UpperCamelCase__      =			True
       UpperCamelCase__      =			True
       UpperCamelCase__      =			True
       UpperCamelCase__      =			True
       UpperCamelCase__      =			99
       UpperCamelCase__      =			32
       UpperCamelCase__      =			2
       UpperCamelCase__      =			4
       UpperCamelCase__      =			37
       UpperCamelCase__      =			"gelu"
       UpperCamelCase__      =			0.1
       UpperCamelCase__      =			0.1
       UpperCamelCase__      =			5_12
       UpperCamelCase__      =			16
       UpperCamelCase__      =			False
       UpperCamelCase__      =			-1
       UpperCamelCase__      =			True
       UpperCamelCase__      =			2
       UpperCamelCase__      =			0.02
       UpperCamelCase__      =			3
       UpperCamelCase__      =			4
       UpperCamelCase__      =			None


   def 	_UpperCamelCase       (  self ):
       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__      =			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     ,						relative_attention=self.relative_attention     ,						max_relative_positions=self.max_relative_positions     ,						position_biased_input=self.position_biased_input     ,						initializer_range=self.initializer_range     ,						return_dict=__UpperCamelCase     ,						)

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


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       UpperCamelCase__      =			TFDebertaModel(config=__UpperCamelCase )
       UpperCamelCase__      =			{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

       UpperCamelCase__      =			[input_ids, input_mask]
       UpperCamelCase__      =			model(__UpperCamelCase )

       UpperCamelCase__      =			model(__UpperCamelCase )

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


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       UpperCamelCase__      =			TFDebertaForMaskedLM(config=__UpperCamelCase )
       UpperCamelCase__      =			{
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }
       UpperCamelCase__      =			model(__UpperCamelCase )
       self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.vocab_size) )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       UpperCamelCase__      =			self.num_labels
       UpperCamelCase__      =			TFDebertaForSequenceClassification(config=__UpperCamelCase )
       UpperCamelCase__      =			{
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }

       UpperCamelCase__      =			model(__UpperCamelCase )
       self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.num_labels) )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       UpperCamelCase__      =			self.num_labels
       UpperCamelCase__      =			TFDebertaForTokenClassification(config=__UpperCamelCase )
       UpperCamelCase__      =			{
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }
       UpperCamelCase__      =			model(__UpperCamelCase )
       self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.num_labels) )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       UpperCamelCase__      =			TFDebertaForQuestionAnswering(config=__UpperCamelCase )
       UpperCamelCase__      =			{
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }

       UpperCamelCase__      =			model(__UpperCamelCase )
       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 ):
       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       lowerCamelCase_							(	lowerCAmelCase_					,    lowerCAmelCase_					,    unittest.TestCase  ):






   '''simple docstring'''

   A :     Optional[Any]				=	(
       (
           TFDebertaModel,
           TFDebertaForMaskedLM,
           TFDebertaForQuestionAnswering,
           TFDebertaForSequenceClassification,
           TFDebertaForTokenClassification,
       )
       if is_tf_available()
       else ()
   )
   A :     List[Any]				=	(
       {
           "feature-extraction": TFDebertaModel,
           "fill-mask": TFDebertaForMaskedLM,
           "question-answering": TFDebertaForQuestionAnswering,
           "text-classification": TFDebertaForSequenceClassification,
           "token-classification": TFDebertaForTokenClassification,
           "zero-shot": TFDebertaForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )

   A :     str				=	False
   A :     List[Any]				=	False


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			TFDebertaModelTester(self )
       UpperCamelCase__      =			ConfigTester(self     ,						config_class=__UpperCamelCase     ,						hidden_size=37 )


   def 	_UpperCamelCase       (  self ):
       self.config_tester.run_common_tests()


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__UpperCamelCase )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase )







   @slow
   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			TFDebertaModel.from_pretrained("kamalkraj/deberta-base" )
       self.assertIsNotNone(__UpperCamelCase )

@require_tf
class       lowerCamelCase_							(	unittest.TestCase  ):






   '''simple docstring'''

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







   @slow
   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			TFDebertaModel.from_pretrained("kamalkraj/deberta-base" )
       UpperCamelCase__      =			tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] )
       UpperCamelCase__      =			tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
       UpperCamelCase__      =			model(__UpperCamelCase     ,						attention_mask=__UpperCamelCase )[0]

       UpperCamelCase__      =			tf.constant(
           [
               [
                   [-0.59_855_896, -0.80_552_566, -0.8_462_135],
                   [1.4_484_025, -0.93_483_794, -0.80_593_085],
                   [0.3_122_741, 0.00_316_059, -1.4_131_377],
               ]
           ] )
       tf.debugging.assert_near(output[:, 1:4, 1:4]     ,						__UpperCamelCase     ,						atol=1e-4 )









'''simple docstring'''







def 						snake_case__ (lowerCAmelCase_	):
    for i, _ in enumerate(lowerCAmelCase_	):
        if (i % 2 == 1) == (nums[i - 1] > nums[i]):
            UpperCamelCase__    ,  UpperCamelCase__      =			nums[i], nums[i - 1]

    return nums


if __name__ == "__main__":
      print('Enter the array elements:')
      lowercase__     :  Optional[int]    					=			list(map(int, input().split()))
      print('The unsorted array is:')
      print(array)
      print('Array after Wiggle sort:')
      print(wiggle_sort(array))




