


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)}")




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__)
