




import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


UpperCAmelCase__				     = logging.get_logger(__name__)







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						def __init__( self :     Any				,							*lowerCamelCase__ :     Optional[int]				,							**lowerCamelCase__ :     str				)    ->						None:
													"""simple docstring"""


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












from __future__ import annotations







def 		_A(       UpperCamelCase__							:			list[int]				,       UpperCamelCase__							:			list[int]				,       UpperCamelCase__							:			int				)    -> tuple[float, list[float]]:
							'''simple docstring'''


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

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

							return max_value, fractions


if __name__ == "__main__":
			import doctest

			doctest.testmod()






