code: 
  
  
  
  
  
  
  """simple docstring"""
  
  import math
  from decimal import Decimal, getcontext
  
  def 	__lowerCamelCase			(						__UpperCamelCase = 4000000 )		->					int:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		try:
  				lowerCAmelCase_     :							List[Any]             =    int(__UpperCamelCase )
  		except (TypeError, ValueError):
  				raise TypeError("Parameter n must be int or castable to int." )
  		if n <= 0:
  				raise ValueError("Parameter n must be greater than or equal to one." )
  		lowerCAmelCase_     :							Union[str, Any]             =    100
  		lowerCAmelCase_     :							str             =    (Decimal(5 ) ** Decimal(0.5 ) + 1) / Decimal(2 )
  
  		lowerCAmelCase_     :							Tuple             =    (math.floor(math.log(n * (phi + 2)   , __UpperCamelCase ) - 1 ) // 3) * 3 + 2
  		lowerCAmelCase_     :							List[Any]             =    Decimal(round(phi ** Decimal(index + 1 ) ) ) / (phi + 2)
  		lowerCAmelCase_     :							Dict             =    num // 2
  		return int(__UpperCamelCase )
  
  
  if __name__ == "__main__":
  					print(F"""{solution() = }""")
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
      is_vision_available,
  )
  
  
  lowercase__      =				{
      """configuration_mobilevitv2""": [
          """MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
          """MobileViTV2Config""",
          """MobileViTV2OnnxConfig""",
      ],
  }
  
  
  try:
  					if not is_torch_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					lowercase__      =				[
  					    """MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
  					    """MobileViTV2ForImageClassification""",
  					    """MobileViTV2ForSemanticSegmentation""",
  					    """MobileViTV2Model""",
  					    """MobileViTV2PreTrainedModel""",
  					]
  
  if TYPE_CHECKING:
  					from .configuration_mobilevitva import (
  					    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
  					    MobileViTVaConfig,
  					    MobileViTVaOnnxConfig,
  					)
  
  					try:
  										if not is_torch_available():
  															raise OptionalDependencyNotAvailable()
  					except OptionalDependencyNotAvailable:
  										pass
  					else:
  										from .modeling_mobilevitva import (
  										    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
  										    MobileViTVaForImageClassification,
  										    MobileViTVaForSemanticSegmentation,
  										    MobileViTVaModel,
  										    MobileViTVaPreTrainedModel,
  										)
  
  else:
  					import sys
  
  					lowercase__      =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  