






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			:
    """simple docstring"""

    def __init__(				self      :     str ,     __SCREAMING_SNAKE_CASE      :     Dict ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=13 ,     __SCREAMING_SNAKE_CASE      :     Dict=7 ,     __SCREAMING_SNAKE_CASE      :     List[Any]=True ,     __SCREAMING_SNAKE_CASE      :     Tuple=True ,     __SCREAMING_SNAKE_CASE      :     List[str]=True ,     __SCREAMING_SNAKE_CASE      :     List[Any]=True ,     __SCREAMING_SNAKE_CASE      :     Any=99 ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=32 ,     __SCREAMING_SNAKE_CASE      :     Dict=2 ,     __SCREAMING_SNAKE_CASE      :     Tuple=4 ,     __SCREAMING_SNAKE_CASE      :     int=37 ,     __SCREAMING_SNAKE_CASE      :     List[str]="gelu" ,     __SCREAMING_SNAKE_CASE      :     str=0.1 ,     __SCREAMING_SNAKE_CASE      :     Any=0.1 ,     __SCREAMING_SNAKE_CASE      :     int=512 ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=16 ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=2 ,     __SCREAMING_SNAKE_CASE      :     List[str]=0.02 ,     __SCREAMING_SNAKE_CASE      :     Dict=False ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=True ,     __SCREAMING_SNAKE_CASE      :     List[Any]="None" ,     __SCREAMING_SNAKE_CASE      :     int=3 ,     __SCREAMING_SNAKE_CASE      :     List[Any]=4 ,     __SCREAMING_SNAKE_CASE      :     List[str]=None ,     )      ->							List[str]:
           __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 _a							(				self      :     str      )      ->							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 _a							(				self      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     Dict ,     __SCREAMING_SNAKE_CASE      :     Dict ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]      )      ->							Any:
           __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 _a							(				self      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     int ,     __SCREAMING_SNAKE_CASE      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     Dict ,     __SCREAMING_SNAKE_CASE      :     List[str]      )      ->							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 _a							(				self      :     Any ,     __SCREAMING_SNAKE_CASE      :     str ,     __SCREAMING_SNAKE_CASE      :     int ,     __SCREAMING_SNAKE_CASE      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     Dict ,     __SCREAMING_SNAKE_CASE      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     Tuple      )      ->							List[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 _a							(				self      :     str ,     __SCREAMING_SNAKE_CASE      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     Dict ,     __SCREAMING_SNAKE_CASE      :     str ,     __SCREAMING_SNAKE_CASE      :     int ,     __SCREAMING_SNAKE_CASE      :     Any ,     __SCREAMING_SNAKE_CASE      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     str      )      ->							Optional[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 _a							(				self      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     int ,     __SCREAMING_SNAKE_CASE      :     Any ,     __SCREAMING_SNAKE_CASE      :     Any ,     __SCREAMING_SNAKE_CASE      :     Optional[Any] ,     __SCREAMING_SNAKE_CASE      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     str ,     __SCREAMING_SNAKE_CASE      :     Any      )      ->							Any:
           __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 _a							(				self      :     int      )      ->							Optional[int]:
           __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				):
    """simple docstring"""

    lowerCamelCase	:							Union[str, Any]				= (
        (
            TFDebertaVaModel,
            TFDebertaVaForMaskedLM,
            TFDebertaVaForQuestionAnswering,
            TFDebertaVaForSequenceClassification,
            TFDebertaVaForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    lowerCamelCase	:							Optional[Any]				= (
        {
            'feature-extraction': TFDebertaVaModel,
            'fill-mask': TFDebertaVaForMaskedLM,
            'question-answering': TFDebertaVaForQuestionAnswering,
            'text-classification': TFDebertaVaForSequenceClassification,
            'token-classification': TFDebertaVaForTokenClassification,
            'zero-shot': TFDebertaVaForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )

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



    def _a							(				self      :     int      )      ->							List[str]:
           __UpperCAmelCase     				=TFDebertaVaModelTester(self      )
           __UpperCAmelCase     				=ConfigTester(self ,     config_class=lowerCamelCase__ ,     hidden_size=37      )



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



    def _a							(				self      :     Any      )      ->							List[Any]:
           __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase__      )



    def _a							(				self      :     Any      )      ->							Optional[int]:
           __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__      )



    def _a							(				self      :     Any      )      ->							List[str]:
           __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__      )



    def _a							(				self      :     List[Any]      )      ->							Dict:
           __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__      )



    def _a							(				self      :     Any      )      ->							List[str]:
           __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__      )



    @slow
    def _a							(				self      :     Union[str, Any]      )      ->							int:
           __UpperCAmelCase     				=TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""      )
           self.assertIsNotNone(lowerCamelCase__      )




@require_tf
class 				_A			(     unittest.TestCase				):
    """simple docstring"""

    @unittest.skip(reason="""Model not available yet"""      )
    def _a							(				self      :     Tuple      )      ->							List[str]:
           pass



    @slow
    def _a							(				self      :     Any      )      ->							Any:
           __UpperCAmelCase     				=TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""      )
           __UpperCAmelCase     				=tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 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      )







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      if len(SCREAMING_SNAKE_CASE   ) < 2:
            return collection

      def circle_sort_util(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ) -> bool:
            A_							       =		False

            if low == high:
                  return swapped

            A_							       =		low
            A_							       =		high

            while left < right:
                  if collection[left] > collection[right]:
                        A_       ,A_							       =		(
                            collection[right],
                            collection[left],
                        )
                        A_							       =		True

                  left += 1
                  right -= 1

            if left == right and collection[left] > collection[right + 1]:
                  A_       ,A_							       =		(
                      collection[right + 1],
                      collection[left],
                  )

                  A_							       =		True

            A_							       =		low + int((high - low) / 2   )
            A_							       =		circle_sort_util(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )
            A_							       =		circle_sort_util(SCREAMING_SNAKE_CASE		,				mid + 1		,				SCREAMING_SNAKE_CASE   )

            return swapped or left_swap or right_swap

      A_							       =		True

      while is_not_sorted is True:
            A_							       =		circle_sort_util(SCREAMING_SNAKE_CASE		,				0		,				len(SCREAMING_SNAKE_CASE   ) - 1   )

      return collection


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





