



"""simple docstring"""


from __future__ import annotations

def  _lowerCAmelCase      (   lowercase_		,	lowercase_		,	lowercase_						):
						UpperCAmelCase             = list(range(len(lowercase_						)						)						)
						UpperCAmelCase             = [v / w for v, w in zip(lowercase_		,	lowercase_						)]
						index.sort(key=lambda lowercase_						: ratio[i]		,	reverse=lowercase_						)

						UpperCAmelCase             = 0
						UpperCAmelCase             = [0] * len(lowercase_						)
						for i in index:
												if weight[i] <= capacity:
																		UpperCAmelCase             = 1
																		max_value += value[i]
																		capacity -= weight[i]
												else:
																		UpperCAmelCase             = capacity / weight[i]
																		max_value += value[i] * capacity / weight[i]
																		break

						return max_value, fractions


if __name__ == "__main__":
				import doctest

				doctest.testmod()













'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A	:			Dict         ={
    '''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A	:			List[Any]         =[
			    '''SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''Swinv2ForImageClassification''',
			    '''Swinv2ForMaskedImageModeling''',
			    '''Swinv2Model''',
			    '''Swinv2PreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swinva import (
						    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwinvaForImageClassification,
						    SwinvaForMaskedImageModeling,
						    SwinvaModel,
						    SwinvaPreTrainedModel,
						)


else:
			import sys

			_A	:			Optional[Any]         =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

