code: 
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_torch_available,
      is_vision_available,
  )
  
  
  __lowerCAmelCase     =			{
      "configuration_segformer": ["SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegformerConfig", "SegformerOnnxConfig"]
  }
  
  try:
  				if not is_vision_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__lowerCAmelCase     =			["SegformerFeatureExtractor"]
  				__lowerCAmelCase     =			["SegformerImageProcessor"]
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__lowerCAmelCase     =			[
  				    "SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "SegformerDecodeHead",
  				    "SegformerForImageClassification",
  				    "SegformerForSemanticSegmentation",
  				    "SegformerLayer",
  				    "SegformerModel",
  				    "SegformerPreTrainedModel",
  				]
  
  try:
  				if not is_tf_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__lowerCAmelCase     =			[
  				    "TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "TFSegformerDecodeHead",
  				    "TFSegformerForImageClassification",
  				    "TFSegformerForSemanticSegmentation",
  				    "TFSegformerModel",
  				    "TFSegformerPreTrainedModel",
  				]
  
  
  if TYPE_CHECKING:
  				from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig
  
  				try:
  								if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .feature_extraction_segformer import SegformerFeatureExtractor
  								from .image_processing_segformer import SegformerImageProcessor
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_segformer import (
  								    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    SegformerDecodeHead,
  								    SegformerForImageClassification,
  								    SegformerForSemanticSegmentation,
  								    SegformerLayer,
  								    SegformerModel,
  								    SegformerPreTrainedModel,
  								)
  				try:
  								if not is_tf_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_tf_segformer import (
  								    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    TFSegformerDecodeHead,
  								    TFSegformerForImageClassification,
  								    TFSegformerForSemanticSegmentation,
  								    TFSegformerModel,
  								    TFSegformerPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				__lowerCAmelCase     =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  def     a						(      _lowercase   :     Optional[int]       )     ->List[str]:
  		UpperCamelCase__         =	len(_lowercase       )
  		for i in range(length - 1       ):
  				UpperCamelCase__         =	False
  				for j in range(length - 1 - i       ):
  						if collection[j] > collection[j + 1]:
  								UpperCamelCase__         =	True
  								UpperCamelCase__  ,			UpperCamelCase__         =	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     =			input("Enter numbers separated by a comma:").strip()
  				__lowerCAmelCase     =			[int(item) for item in user_input.split(",")]
  				__lowerCAmelCase     =			time.process_time()
  				print(*bubble_sort(unsorted), sep=",")
  				print(f"""Processing time: {(time.process_time() - start)%1e9 + 7}""")
  
  
  
  
  
  