
'''simple docstring'''





import math





def        snake_case							(		UpperCAmelCase	)->			float:







						"""simple docstring"""
						__A       	=			math.log(2      ,  1_0	) * number
						__A       	=			round((log_number - int(UpperCAmelCase	))      ,  1_5	)

						return difference





def        snake_case							(		UpperCAmelCase = 6_7_8_9_1_0	)->			int:







						"""simple docstring"""
						__A       	=			9_0
						__A       	=			0

						__A       	=			math.log(1.23      ,  1_0	)
						__A       	=			math.log(1.24      ,  1_0	)
						__A       	=			0

						while position < number:
												__A       	=			log_difference(UpperCAmelCase	)

												if difference >= upper_limit:
																		power_iterator += 9_3

												elif difference < lower_limit:
																		power_iterator += 1_9_6

												else:
																		__A       	=			power_iterator
																		power_iterator += 1_9_6
																		position += 1

						return previous_power


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	print(f'''{solution() = }''')








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


a__		:   Optional[Any]            =       {
    "configuration_mobilevitv2": [
        "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileViTV2Config",
        "MobileViTV2OnnxConfig",
    ],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a__		:   Optional[int]            =       [
     "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

 a__		:   List[str]            =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






