

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     lowercase_       :



      def __init__(     self :							Tuple     ,   A__ :							Union[str, Any]     ,   A__ :							Optional[int]=13     ,   A__ :							List[str]=7     ,   A__ :							Optional[Any]=True     ,   A__ :							int=True     ,   A__ :							Optional[int]=True     ,   A__ :							Optional[int]=True     ,   A__ :							List[str]=99     ,   A__ :							str=32     ,   A__ :							int=2     ,   A__ :							Optional[Any]=4     ,   A__ :							Dict=37     ,   A__ :							Optional[int]="gelu"     ,   A__ :							Tuple=0.1     ,   A__ :							Tuple=0.1     ,   A__ :							Optional[Any]=512     ,   A__ :							Union[str, Any]=16     ,   A__ :							Tuple=2     ,   A__ :							Any=0.02     ,   A__ :							int=3     ,   A__ :							Optional[int]=4     ,   A__ :							str=None     ,   )       ->	Optional[Any]:
        _snake_case		=				parent
        _snake_case		=				13
        _snake_case		=				7
        _snake_case		=				True
        _snake_case		=				True
        _snake_case		=				True
        _snake_case		=				True
        _snake_case		=				99
        _snake_case		=				32
        _snake_case		=				2
        _snake_case		=				4
        _snake_case		=				37
        _snake_case		=				'''gelu'''
        _snake_case		=				0.1
        _snake_case		=				0.1
        _snake_case		=				512
        _snake_case		=				16
        _snake_case		=				False
        _snake_case		=				-1
        _snake_case		=				True
        _snake_case		=				2
        _snake_case		=				0.02
        _snake_case		=				3
        _snake_case		=				4
        _snake_case		=				None



      def 		UpperCamelCase_						(     self :							str						)       ->	Tuple:
        _snake_case		=				ids_tensor([self.batch_size, self.seq_length]     ,   self.vocab_size						)

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

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

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

        _snake_case		=				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=A__     ,   )

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



      def 		UpperCamelCase_						(     self :							Union[str, Any]     ,   A__ :							str     ,   A__ :							Dict     ,   A__ :							List[Any]     ,   A__ :							Any     ,   A__ :							Optional[Any]     ,   A__ :							Tuple     ,   A__ :							Union[str, Any]						)       ->	str:
        _snake_case		=				TFDebertaModel(config=A__						)
        _snake_case		=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

        _snake_case		=				[input_ids, input_mask]
        _snake_case		=				model(A__						)

        _snake_case		=				model(A__						)

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



      def 		UpperCamelCase_						(     self :							Dict     ,   A__ :							List[Any]     ,   A__ :							Any     ,   A__ :							Optional[int]     ,   A__ :							Union[str, Any]     ,   A__ :							Dict     ,   A__ :							Optional[int]     ,   A__ :							Optional[int]						)       ->	Dict:
        _snake_case		=				TFDebertaForMaskedLM(config=A__						)
        _snake_case		=				{
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
            '''token_type_ids''': token_type_ids,
        }
        _snake_case		=				model(A__						)
        self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.seq_length, self.vocab_size)						)



      def 		UpperCamelCase_						(     self :							str     ,   A__ :							Tuple     ,   A__ :							Any     ,   A__ :							List[str]     ,   A__ :							List[Any]     ,   A__ :							Union[str, Any]     ,   A__ :							Dict     ,   A__ :							Dict						)       ->	Optional[Any]:
        _snake_case		=				self.num_labels
        _snake_case		=				TFDebertaForSequenceClassification(config=A__						)
        _snake_case		=				{
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
            '''token_type_ids''': token_type_ids,
        }

        _snake_case		=				model(A__						)
        self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.num_labels)						)



      def 		UpperCamelCase_						(     self :							Optional[int]     ,   A__ :							Any     ,   A__ :							Optional[int]     ,   A__ :							Any     ,   A__ :							int     ,   A__ :							Union[str, Any]     ,   A__ :							int     ,   A__ :							List[Any]						)       ->	int:
        _snake_case		=				self.num_labels
        _snake_case		=				TFDebertaForTokenClassification(config=A__						)
        _snake_case		=				{
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
            '''token_type_ids''': token_type_ids,
        }
        _snake_case		=				model(A__						)
        self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.seq_length, self.num_labels)						)



      def 		UpperCamelCase_						(     self :							List[str]     ,   A__ :							int     ,   A__ :							Optional[Any]     ,   A__ :							int     ,   A__ :							List[str]     ,   A__ :							List[str]     ,   A__ :							Optional[Any]     ,   A__ :							Optional[Any]						)       ->	int:
        _snake_case		=				TFDebertaForQuestionAnswering(config=A__						)
        _snake_case		=				{
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
            '''token_type_ids''': token_type_ids,
        }

        _snake_case		=				model(A__						)
        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 :							List[str]						)       ->	Optional[Any]:
        _snake_case		=				self.prepare_config_and_inputs()
        (
            (
            _snake_case
        ),	(
            _snake_case
        ),	(
            _snake_case
        ),	(
            _snake_case
        ),	(
            _snake_case
        ),	(
            _snake_case
        ),	(
            _snake_case
        ),	
        )		=				config_and_inputs
        _snake_case		=				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
        return config, inputs_dict



@require_tf
class     lowercase_       (    __lowercase  ,				__lowercase  ,				unittest.TestCase	):
      UpperCamelCase_     :						Union[str, Any]		 =							(
          (
              TFDebertaModel,
              TFDebertaForMaskedLM,
              TFDebertaForQuestionAnswering,
              TFDebertaForSequenceClassification,
              TFDebertaForTokenClassification,
          )
          if is_tf_available()
          else ()
      )
      UpperCamelCase_     :						Dict		 =							(
          {
              "feature-extraction": TFDebertaModel,
              "fill-mask": TFDebertaForMaskedLM,
              "question-answering": TFDebertaForQuestionAnswering,
              "text-classification": TFDebertaForSequenceClassification,
              "token-classification": TFDebertaForTokenClassification,
              "zero-shot": TFDebertaForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )

      UpperCamelCase_     :						Union[str, Any]		 =							False
      UpperCamelCase_     :						Tuple		 =							False



      def 		UpperCamelCase_						(     self :							Optional[int]						)       ->	List[str]:
        _snake_case		=				TFDebertaModelTester(self						)
        _snake_case		=				ConfigTester(self     ,   config_class=A__     ,   hidden_size=37						)



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



      def 		UpperCamelCase_						(     self :							Tuple						)       ->	Union[str, Any]:
        _snake_case		=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A__						)



      def 		UpperCamelCase_						(     self :							Dict						)       ->	int:
        _snake_case		=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*A__						)



      def 		UpperCamelCase_						(     self :							List[str]						)       ->	Tuple:
        _snake_case		=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*A__						)



      def 		UpperCamelCase_						(     self :							List[str]						)       ->	List[str]:
        _snake_case		=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*A__						)



      def 		UpperCamelCase_						(     self :							Optional[int]						)       ->	Any:
        _snake_case		=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*A__						)







      @slow
      def 		UpperCamelCase_						(     self :							List[Any]						)       ->	Optional[Any]:
        _snake_case		=				TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''						)
        self.assertIsNotNone(A__						)



@require_tf
class     lowercase_       (    unittest.TestCase	):



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







      @slow
      def 		UpperCamelCase_						(     self :							Union[str, Any]						)       ->	Optional[Any]:
        _snake_case		=				TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''						)
        _snake_case		=				tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]						)
        _snake_case		=				tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
        _snake_case		=				model(A__     ,   attention_mask=A__						)[0]

        _snake_case		=				tf.constant(
            [
                [
                    [-0.5985_5896, -0.8055_2566, -0.846_2135],
                    [1.448_4025, -0.9348_3794, -0.8059_3085],
                    [0.312_2741, 0.0031_6059, -1.413_1377],
                ]
            ]						)
        tf.debugging.assert_near(output[:, 1:4, 1:4]     ,   A__     ,   atol=1e-4						)




def  snake_case_(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)				->      Optional[Any]:



  """simple docstring"""






  _snake_case		=				index
  _snake_case		=				2 * index + 1
  _snake_case		=				2 * index + 2
  if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
    _snake_case		=				left_index

  if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
    _snake_case		=				right_index

  if largest != index:
    _snake_case,	_snake_case		=				unsorted[index], unsorted[largest]
    heapify(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)



def  snake_case_(_UpperCamelCase	)				->      Any:



  """simple docstring"""






  _snake_case		=				len(_UpperCamelCase	)
  for i in range(n // 2 - 1					,			-1					,			-1	):
    heapify(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)
  for i in range(n - 1					,			0					,			-1	):
    _snake_case,	_snake_case		=				unsorted[i], unsorted[0]
    heapify(_UpperCamelCase					,			0					,			_UpperCamelCase	)
  return unsorted


if __name__ == "__main__":
 __A     						=      input('''Enter numbers separated by a comma:\n''').strip()
 __A     						=      [int(item) for item in user_input.split(''',''')]
 print(heap_sort(unsorted))

