


import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
    from transformers.models.roberta.modeling_flax_roberta import (
        FlaxRobertaForCausalLM,
        FlaxRobertaForMaskedLM,
        FlaxRobertaForMultipleChoice,
        FlaxRobertaForQuestionAnswering,
        FlaxRobertaForSequenceClassification,
        FlaxRobertaForTokenClassification,
        FlaxRobertaModel,
    )


class     lowercase__  (					unittest.TestCase		):






  def __init__(					self      :					int		,						_lowercase      :					Dict		,						_lowercase      :					Tuple=13		,						_lowercase      :					List[str]=7		,						_lowercase      :					int=True		,						_lowercase      :					int=True		,						_lowercase      :					List[Any]=True		,						_lowercase      :					Optional[Any]=True		,						_lowercase      :					int=99		,						_lowercase      :					Dict=32		,						_lowercase      :					Dict=5		,						_lowercase      :					int=4		,						_lowercase      :					Dict=37		,						_lowercase      :					int="gelu"		,						_lowercase      :					List[str]=0.1		,						_lowercase      :					List[str]=0.1		,						_lowercase      :					Dict=5_12		,						_lowercase      :					int=16		,						_lowercase      :					Optional[Any]=2		,						_lowercase      :					List[Any]=0.0_2		,						_lowercase      :					Any=4		,						):




         """simple docstring"""







         UpperCAmelCase__										=     parent
         UpperCAmelCase__										=     batch_size
         UpperCAmelCase__										=     seq_length
         UpperCAmelCase__										=     is_training
         UpperCAmelCase__										=     use_attention_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_choices






  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     ids_tensor([self.batch_size, self.seq_length]		,						self.vocab_size )

         UpperCAmelCase__										=     None
         if self.use_attention_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__										=     RobertaConfig(
             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		,						is_decoder=snake_case_		,						initializer_range=self.initializer_range		,						)

         return config, input_ids, token_type_ids, attention_mask






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     self.prepare_config_and_inputs()
         UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__										=     config_and_inputs
         UpperCAmelCase__										=     {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
         return config, inputs_dict






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.prepare_config_and_inputs()
         UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__										=     config_and_inputs

         UpperCAmelCase__										=     True
         UpperCAmelCase__										=     floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
         UpperCAmelCase__										=     ids_tensor([self.batch_size, self.seq_length]		,						vocab_size=2 )

         return (
             config,
             input_ids,
             token_type_ids,
             encoder_hidden_states,
             encoder_attention_mask,
         )




@require_flax
class     lowercase__  (					_a					,							unittest.TestCase		):
  A__=  True

  A__=  (
      (
          FlaxRobertaModel,
          FlaxRobertaForCausalLM,
          FlaxRobertaForMaskedLM,
          FlaxRobertaForSequenceClassification,
          FlaxRobertaForTokenClassification,
          FlaxRobertaForMultipleChoice,
          FlaxRobertaForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         UpperCAmelCase__										=     FlaxRobertaModelTester(self )






  @slow
  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         for model_class_name in self.all_model_classes:
                UpperCAmelCase__										=     model_class_name.from_pretrained("roberta-base"		,						from_pt=snake_case_ )
                UpperCAmelCase__										=     model(np.ones((1, 1) ) )
                self.assertIsNotNone(snake_case_ )









def __UpperCAmelCase       (		__A			)						->					list[int]:



       '''simple docstring'''





       UpperCAmelCase__										=     len(__A			)
       for i in range(__A			):
              for j in range(i + 1 ,						__A			):
                     if numbers[j] < numbers[i]:
                            UpperCAmelCase__						,			UpperCAmelCase__										=     numbers[j], numbers[i]
       return numbers


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





