code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  a__				: Any									=    {
      '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
      '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
  }
  
  try:
  			if not is_tokenizers_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			a__				: str									=    ['''DebertaV2TokenizerFast''']
  
  try:
  			if not is_tf_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			a__				: int									=    [
  			    '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
  			    '''TFDebertaV2ForMaskedLM''',
  			    '''TFDebertaV2ForQuestionAnswering''',
  			    '''TFDebertaV2ForSequenceClassification''',
  			    '''TFDebertaV2ForTokenClassification''',
  			    '''TFDebertaV2Model''',
  			    '''TFDebertaV2PreTrainedModel''',
  			]
  
  try:
  			if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			a__				: Tuple									=    [
  			    '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
  			    '''DebertaV2ForMaskedLM''',
  			    '''DebertaV2ForMultipleChoice''',
  			    '''DebertaV2ForQuestionAnswering''',
  			    '''DebertaV2ForSequenceClassification''',
  			    '''DebertaV2ForTokenClassification''',
  			    '''DebertaV2Model''',
  			    '''DebertaV2PreTrainedModel''',
  			]
  
  
  if TYPE_CHECKING:
  			from .configuration_deberta_va import (
  			    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
  			    DebertaVaConfig,
  			    DebertaVaOnnxConfig,
  			)
  			from .tokenization_deberta_va import DebertaVaTokenizer
  
  			try:
  						if not is_tokenizers_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .tokenization_deberta_va_fast import DebertaVaTokenizerFast
  
  			try:
  						if not is_tf_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .modeling_tf_deberta_va import (
  						    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
  						    TFDebertaVaForMaskedLM,
  						    TFDebertaVaForQuestionAnswering,
  						    TFDebertaVaForSequenceClassification,
  						    TFDebertaVaForTokenClassification,
  						    TFDebertaVaModel,
  						    TFDebertaVaPreTrainedModel,
  						)
  
  			try:
  						if not is_torch_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .modeling_deberta_va import (
  						    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
  						    DebertaVaForMaskedLM,
  						    DebertaVaForMultipleChoice,
  						    DebertaVaForQuestionAnswering,
  						    DebertaVaForSequenceClassification,
  						    DebertaVaForTokenClassification,
  						    DebertaVaModel,
  						    DebertaVaPreTrainedModel,
  						)
  
  else:
  			import sys
  
  			a__				: Dict									=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_       )      ->str:
  	snake_case__  					=      len(UpperCAmelCase_       )
  	for i in range(length - 1       ):
  		snake_case__  					=      False
  		for j in range(length - 1 - i       ):
  			if collection[j] > collection[j + 1]:
  				snake_case__  					=      True
  				snake_case__					,     snake_case__  					=      collection[j + 1], collection[j]
  		if not swapped:
  			break  # Stop iteration if the collection is sorted.
  	return collection
  
  
  if __name__ == "__main__":
  			import doctest
  			import time
  
  			doctest.testmod()
  
  			a__				: Dict									=    input('''Enter numbers separated by a comma:''').strip()
  			a__				: Dict									=    [int(item) for item in user_input.split(''',''')]
  			a__				: Tuple									=    time.process_time()
  			print(*bubble_sort(unsorted), sep=''',''')
  			print(f"""Processing time: {(time.process_time() - start)%1e9 + 7}""")
  
  
  
  