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 __snake_case    (						unittest.TestCase		):




			def __init__(   self      :    str	,     _lowercase      :    str	,     _lowercase      :    int=13	,     _lowercase      :    Union[str, Any]=7	,     _lowercase      :    List[str]=True	,     _lowercase      :    Any=True	,     _lowercase      :    str=True	,     _lowercase      :    Any=True	,     _lowercase      :    Union[str, Any]=99	,     _lowercase      :    int=32	,     _lowercase      :    int=5	,     _lowercase      :    int=4	,     _lowercase      :    Any=37	,     _lowercase      :    Optional[int]="gelu"	,     _lowercase      :    List[Any]=0.1	,     _lowercase      :    List[str]=0.1	,     _lowercase      :    Optional[Any]=5_12	,     _lowercase      :    int=16	,     _lowercase      :    Dict=2	,     _lowercase      :    int=0.02	,     _lowercase      :    Dict=4	,     ):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__      							=				parent
					SCREAMING_SNAKE_CASE__      							=				batch_size
					SCREAMING_SNAKE_CASE__      							=				seq_length
					SCREAMING_SNAKE_CASE__      							=				is_training
					SCREAMING_SNAKE_CASE__      							=				use_attention_mask
					SCREAMING_SNAKE_CASE__      							=				use_token_type_ids
					SCREAMING_SNAKE_CASE__      							=				use_labels
					SCREAMING_SNAKE_CASE__      							=				vocab_size
					SCREAMING_SNAKE_CASE__      							=				hidden_size
					SCREAMING_SNAKE_CASE__      							=				num_hidden_layers
					SCREAMING_SNAKE_CASE__      							=				num_attention_heads
					SCREAMING_SNAKE_CASE__      							=				intermediate_size
					SCREAMING_SNAKE_CASE__      							=				hidden_act
					SCREAMING_SNAKE_CASE__      							=				hidden_dropout_prob
					SCREAMING_SNAKE_CASE__      							=				attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE__      							=				max_position_embeddings
					SCREAMING_SNAKE_CASE__      							=				type_vocab_size
					SCREAMING_SNAKE_CASE__      							=				type_sequence_label_size
					SCREAMING_SNAKE_CASE__      							=				initializer_range
					SCREAMING_SNAKE_CASE__      							=				num_choices




			def      __a						(   self      :    Optional[Any]				):



					"""simple docstring"""






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

					SCREAMING_SNAKE_CASE__      							=				None
					if self.use_attention_mask:
							SCREAMING_SNAKE_CASE__      							=				random_attention_mask([self.batch_size, self.seq_length]				)

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

					SCREAMING_SNAKE_CASE__      							=				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=_lowercase	,     initializer_range=self.initializer_range	,     )

					return config, input_ids, token_type_ids, attention_mask




			def      __a						(   self      :    Optional[int]				):



					"""simple docstring"""






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




			def      __a						(   self      :    str				):



					"""simple docstring"""






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

					SCREAMING_SNAKE_CASE__      							=				True
					SCREAMING_SNAKE_CASE__      							=				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
					SCREAMING_SNAKE_CASE__      							=				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 __snake_case    (						lowerCamelCase_       ,		unittest.TestCase		):
			lowerCAmelCase_														=							True

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




			def      __a						(   self      :    int				):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__      							=				FlaxRobertaModelTester(self				)




			@slow
			def      __a						(   self      :    Optional[int]				):



					"""simple docstring"""






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

def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Union[str, Any]     )							-> int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )
  for i in range(length - 1     ):
    SCREAMING_SNAKE_CASE__      							=				i
    for k in range(i + 1			,					__UpperCamelCase     ):
      if collection[k] < collection[least]:
        SCREAMING_SNAKE_CASE__      							=				k
    if least != i:
      SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				(collection[i], collection[least])
  return collection


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