code: 
  
  
  from __future__ import annotations
  
  import sys
  def 							_UpperCAmelCase       (UpperCamelCase__  :						int		):
         if n < 0:
                raise ValueError("Negative arguments are not supported"		)
         return _fib(UpperCamelCase__		)[0]
  def 							_UpperCAmelCase       (UpperCamelCase__  :						int		):
         if n == 0:  # (F(0), F(1))
                return (0, 1)
  
         # F(2n) = F(n)[2F(n+1) − F(n)]
         # F(2n+1) = F(n+1)^2+F(n)^2
         _A					,    _A   : int       					=				_fib(n // 2		)
         _A   : Tuple       					=				a * (b * 2 - a)
         _A   : List[Any]       					=				a * a + b * b
         return (d, c + d) if n % 2 else (c, d)
  
  
  if __name__ == "__main__":
      lowerCAmelCase__   	=				int(sys.argv[1])
      print(f"fibonacci({n}) is {fibonacci(n)}")
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  lowerCAmelCase__   	=				{'configuration_detr': ['DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetrConfig', 'DetrOnnxConfig']}
  
  try:
      if not is_vision_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				['DetrFeatureExtractor']
      lowerCAmelCase__   	=				['DetrImageProcessor']
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          'DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
          'DetrForObjectDetection',
          'DetrForSegmentation',
          'DetrModel',
          'DetrPreTrainedModel',
      ]
  
  
  if TYPE_CHECKING:
      from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig
  
      try:
          if not is_vision_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .feature_extraction_detr import DetrFeatureExtractor
          from .image_processing_detr import DetrImageProcessor
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_detr import (
              DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
              DetrForObjectDetection,
              DetrForSegmentation,
              DetrModel,
              DetrPreTrainedModel,
          )
  
  else:
      import sys
  
      lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  