




'''simple docstring'''


import math
from decimal import Decimal, getcontext
def 		snake_case_    (UpperCamelCase       :  int = 400_0000      ):





							'''simple docstring'''


							try:
														_a	     =				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.'''      )
							_a	     =				100
							_a	     =				(Decimal(5      ) ** Decimal(0.5      ) + 1) / Decimal(2      )

							_a	     =				(math.floor(math.log(n * (phi + 2) ,						UpperCamelCase      ) - 1      ) // 3) * 3 + 2
							_a	     =				Decimal(round(phi ** Decimal(index + 1      )      )      ) / (phi + 2)
							_a	     =				num // 2
							return int(UpperCamelCase      )


if __name__ == "__main__":
				print(F'''{solution() = }''')







'''simple docstring'''


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_snake_case      :      Tuple   							=		{
    'configuration_convnextv2': [
        'CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConvNextV2Config',
    ]
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case      :      Tuple   							=		[
				    'CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ConvNextV2ForImageClassification',
				    'ConvNextV2Model',
				    'ConvNextV2PreTrainedModel',
				    'ConvNextV2Backbone',
				]


if TYPE_CHECKING:
				from .configuration_convnextva import (
				    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    ConvNextVaConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_convnextva import (
								    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ConvNextVaBackbone,
								    ConvNextVaForImageClassification,
								    ConvNextVaModel,
								    ConvNextVaPreTrainedModel,
								)

else:
				import sys

				_snake_case      :      Any   							=		_LazyModule(__name__, globals()['__file__'], _import_structure)

