


from __future__ import annotations






def 				UpperCamelCase      (			__magic_name__  :							int					,					__magic_name__  :							list[int]					,					__magic_name__  :							list[int]					,					__magic_name__  :							int  )     ->						int:
		"""simple docstring"""




		if counter == 0 or capacity == 0:
				return 0

		# If weight of the nth item is more than Knapsack of capacity,
		#   then this item cannot be included in the optimal solution,
		# else return the maximum of two cases:
		#   (1) nth item included
		#   (2) not included
		if weights[counter - 1] > capacity:
				return knapsack(__magic_name__					,					__magic_name__					,					__magic_name__					,					counter - 1  )
		else:
				lowercase__      =	capacity - weights[counter - 1]
				lowercase__      =	values[counter - 1] + knapsack(
				    __magic_name__					,					__magic_name__					,					__magic_name__					,					counter - 1  )
				lowercase__      =	knapsack(__magic_name__					,					__magic_name__					,					__magic_name__					,					counter - 1  )
				return max(__magic_name__					,					__magic_name__  )


if __name__ == "__main__":
	import doctest

	doctest.testmod()





import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


A :	Optional[Any]												=     logging.get_logger(__name__)



class 							A					( UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self					:		Optional[int]   ,      *_UpperCAmelCase					:		Union[str, Any]   ,      **_UpperCAmelCase					:		Any	)		->     None:




							"""simple docstring"""


							warnings.warn(
							    """The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
							    """ Please use MobileNetV1ImageProcessor instead."""   ,      _UpperCAmelCase   ,      )
							super().__init__(*_UpperCAmelCase   ,      **_UpperCAmelCase	)

