




'''simple docstring'''


from __future__ import annotations

from enum import Enum

class 			A    (		_a    ):
							lowercase_							=				24
							lowercase_							=				21
							lowercase_							=				18
							lowercase_							=				15
							lowercase_							=				12
							lowercase_							=				9
							lowercase_							=				6
							lowercase_							=				3
							lowercase_							=				2
							lowercase_							=				1
							lowercase_							=				-1
							lowercase_							=				-2
							lowercase_							=				-3
							lowercase_							=				-6
							lowercase_							=				-9
							lowercase_							=				-12
							lowercase_							=				-15
							lowercase_							=				-18
							lowercase_							=				-21
							lowercase_							=				-24



class 			A    (		_a    ):
							lowercase_							=				8
							lowercase_							=				7
							lowercase_							=				6
							lowercase_							=				5
							lowercase_							=				4
							lowercase_							=				3
							lowercase_							=				2
							lowercase_							=				1



def 		snake_case_    (UpperCamelCase       :  float ,						UpperCamelCase       :  str | SIUnit ,						UpperCamelCase       :  str | SIUnit ,						):





							'''simple docstring'''


							if isinstance(UpperCamelCase ,						UpperCamelCase      ):
														_a	     =				SIUnit[known_prefix.lower()]
							if isinstance(UpperCamelCase ,						UpperCamelCase      ):
														_a	     =				SIUnit[unknown_prefix.lower()]
							_a	     =				known_amount * (
							    10 ** (known_prefix.value - unknown_prefix.value)
							)
							return unknown_amount
def 		snake_case_    (UpperCamelCase       :  float ,						UpperCamelCase       :  str | BinaryUnit ,						UpperCamelCase       :  str | BinaryUnit ,						):





							'''simple docstring'''


							if isinstance(UpperCamelCase ,						UpperCamelCase      ):
														_a	     =				BinaryUnit[known_prefix.lower()]
							if isinstance(UpperCamelCase ,						UpperCamelCase      ):
														_a	     =				BinaryUnit[unknown_prefix.lower()]
							_a	     =				known_amount * (
							    2 ** ((known_prefix.value - unknown_prefix.value) * 10)
							)
							return unknown_amount


if __name__ == "__main__":
				import doctest

				doctest.testmod()







'''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)

