





from __future__ import annotations

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Dict  						=   list(range(len(__lowerCAmelCase ) ) )
				_snake_case :			Optional[int]  						=   [v / w for v, w in zip(__lowerCAmelCase	,			__lowerCAmelCase )]
				index.sort(key=lambda __lowerCAmelCase : ratio[i]	,			reverse=__lowerCAmelCase )

				_snake_case :			float  						=   0
				_snake_case :			list[float]  						=   [0] * len(__lowerCAmelCase )
				for i in index:
								if weight[i] <= capacity:
												_snake_case :			List[Any]  						=   1
												max_value += value[i]
												capacity -= weight[i]
								else:
												_snake_case :			Any  						=   capacity / weight[i]
												max_value += value[i] * capacity / weight[i]
												break

				return max_value, fractions


if __name__ == "__main__":
		import doctest

		doctest.testmod()













import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


lowercase_		:				str				=						logging.get_logger(__name__)


class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		int ,				*lowerCamelCase_			:		str ,				**lowerCamelCase_			:		Tuple					):





										'''simple docstring'''







										warnings.warn(
										    'The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
										    ' Please use SegformerImageProcessor instead.' ,				lowerCamelCase_ ,				)
										super().__init__(*lowerCamelCase_ ,				**lowerCamelCase_					)






