





from functools import lru_cache




@lru_cache
def  A_(      A ):
	if not isinstance(A					,   A ):
		raise TypeError("""k must be an integer.""" )
	if k < 0:
		raise ValueError("""k integer must be greater or equal to zero.""" )
	if k == 0:
		return 0
	elif k == 1:
		return 1
	else:
		return fibonacci(k - 1 ) + fibonacci(k - 2 )




def  A_(      A					,   A ):
	UpperCAmelCase_		    =  len(A )
	# Find m such that F_m >= n where F_i is the i_th fibonacci number.
	UpperCAmelCase_		    =  0
	while True:
		if fibonacci(A ) >= len_list:
			UpperCAmelCase_		    =  i
			break
		i += 1
	UpperCAmelCase_		    =  0
	while fibb_k > 0:
		UpperCAmelCase_		    =  min(
		    offset + fibonacci(fibb_k - 1 )					,   len_list - 1 )  # Prevent out of range
		UpperCAmelCase_		    =  arr[index_k]
		if item_k_1 == val:
			return index_k
		elif val < item_k_1:
			fibb_k -= 1
		elif val > item_k_1:
			offset += fibonacci(fibb_k - 1 )
			fibb_k -= 2
	else:
		return -1


if __name__ == "__main__":
				import doctest

				doctest.testmod()









from typing import TYPE_CHECKING

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


UpperCamelCase__ :			Union[str, Any]       	=			{
    """configuration_mobilevitv2""": [
        """MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileViTV2Config""",
        """MobileViTV2OnnxConfig""",
    ],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__ :			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

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


