code: 
  
  
  from __future__ import annotations
  
  import sys
  def 							_UpperCAmelCase       (UpperCamelCase__  :						int       ,							UpperCamelCase__  :						int		):
         # base cases
         if abs(UpperCamelCase__		) == 1:
                return a, 0
         elif abs(UpperCamelCase__		) == 1:
                return 0, b
  
         _A					,    _A   : int       					=				a, b
         _A					,    _A   : Union[str, Any]       					=				1, 0
         _A					,    _A   : Optional[int]       					=				0, 1
  
         while remainder != 0:
                _A   : str       					=				old_remainder // remainder
                _A					,    _A   : int       					=				remainder, old_remainder - quotient * remainder
                _A					,    _A   : List[Any]       					=				coeff_a, old_coeff_a - quotient * coeff_a
                _A					,    _A   : Tuple       					=				coeff_b, old_coeff_b - quotient * coeff_b
  
         # sign correction for negative numbers
         if a < 0:
                _A   : Optional[int]       					=				-old_coeff_a
         if b < 0:
                _A   : int       					=				-old_coeff_b
  
         return old_coeff_a, old_coeff_b
  def 							_UpperCAmelCase       ():
         if len(sys.argv		) < 3:
                print("2 integer arguments required"		)
                return 1
         _A   : Dict       					=				int(sys.argv[1]		)
         _A   : Tuple       					=				int(sys.argv[2]		)
         print(extended_euclidean_algorithm(UpperCamelCase__       ,							UpperCamelCase__		)		)
         return 0
  
  
  if __name__ == "__main__":
      raise SystemExit(main())
  
style_context: 
  
  
  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__)
  