


def 		a__       (							A__ = 1_0_0_0							):
			SCREAMING_SNAKE_CASE_						:			Optional[int]       						= 0
			SCREAMING_SNAKE_CASE_						:			str       						= (n - 1) // 3
			total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
			SCREAMING_SNAKE_CASE_						:			List[Any]       						= (n - 1) // 5
			total += ((terms) * (1_0 + (terms - 1) * 5)) // 2
			SCREAMING_SNAKE_CASE_						:			Dict       						= (n - 1) // 1_5
			total -= ((terms) * (3_0 + (terms - 1) * 1_5)) // 2
			return total


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)

