code: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __lowerCamelCase       :       Any				   =			{
      "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
  }
  
  try:
  						if not is_sentencepiece_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						__lowerCamelCase       :       str				   =			["CamembertTokenizer"]
  
  try:
  						if not is_tokenizers_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						__lowerCamelCase       :       Tuple				   =			["CamembertTokenizerFast"]
  
  try:
  						if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						__lowerCamelCase       :       Tuple				   =			[
  						    "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
  						    "CamembertForCausalLM",
  						    "CamembertForMaskedLM",
  						    "CamembertForMultipleChoice",
  						    "CamembertForQuestionAnswering",
  						    "CamembertForSequenceClassification",
  						    "CamembertForTokenClassification",
  						    "CamembertModel",
  						    "CamembertPreTrainedModel",
  						]
  
  try:
  						if not is_tf_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						__lowerCamelCase       :       Tuple				   =			[
  						    "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
  						    "TFCamembertForCausalLM",
  						    "TFCamembertForMaskedLM",
  						    "TFCamembertForMultipleChoice",
  						    "TFCamembertForQuestionAnswering",
  						    "TFCamembertForSequenceClassification",
  						    "TFCamembertForTokenClassification",
  						    "TFCamembertModel",
  						    "TFCamembertPreTrainedModel",
  						]
  
  
  if TYPE_CHECKING:
  						from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig
  
  						try:
  												if not is_sentencepiece_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .tokenization_camembert import CamembertTokenizer
  
  						try:
  												if not is_tokenizers_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .tokenization_camembert_fast import CamembertTokenizerFast
  
  						try:
  												if not is_torch_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .modeling_camembert import (
  												    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  												    CamembertForCausalLM,
  												    CamembertForMaskedLM,
  												    CamembertForMultipleChoice,
  												    CamembertForQuestionAnswering,
  												    CamembertForSequenceClassification,
  												    CamembertForTokenClassification,
  												    CamembertModel,
  												    CamembertPreTrainedModel,
  												)
  
  						try:
  												if not is_tf_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .modeling_tf_camembert import (
  												    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  												    TFCamembertForCausalLM,
  												    TFCamembertForMaskedLM,
  												    TFCamembertForMultipleChoice,
  												    TFCamembertForQuestionAnswering,
  												    TFCamembertForSequenceClassification,
  												    TFCamembertForTokenClassification,
  												    TFCamembertModel,
  												    TFCamembertPreTrainedModel,
  												)
  
  else:
  						import sys
  
  						__lowerCamelCase       :       Tuple				   =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
style_context: 
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Dict:
  				A__     :							Dict   				=len(snake_case_	)
  				for i in range(length - 1	):
  								A__     :							List[Any]   				=False
  								for j in range(length - 1 - i	):
  												if collection[j] > collection[j + 1]:
  																A__     :							Dict   				=True
  																A__				,  A__     :							Union[str, Any]   				=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()
  
  						__lowerCamelCase       :       Union[str, Any]				   =			input("Enter numbers separated by a comma:").strip()
  						__lowerCamelCase       :       Optional[int]				   =			[int(item) for item in user_input.split(",")]
  						__lowerCamelCase       :       Optional[int]				   =			time.process_time()
  						print(*bubble_sort(unsorted), sep=",")
  						print(F"Processing time: {(time.process_time() - start)%1E9 + 7}")
  
  
  
  