
from __future__ import annotations







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		int:

				# Base Case
				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(lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			counter - 1  )
				else:
								UpperCamelCase_:							Optional[int]		=  capacity - weights[counter - 1]
								UpperCamelCase_:							str		=  values[counter - 1] + knapsack(
								    lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			counter - 1  )
								UpperCamelCase_:							Any		=  knapsack(lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			counter - 1  )
								return max(lowerCamelCase   ,			lowerCamelCase  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()








import warnings

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


lowerCamelCase_  :       List[Any]					      =     logging.get_logger(__name__)

class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			def __init__(				self       :				Dict  ,		*snake_case_       :				Optional[int]  ,		**snake_case_       :				List[Any]					):
							warnings.warn(
							    """The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
							    """ Please use MobileNetV2ImageProcessor instead."""  ,		snake_case_  ,		)
							super().__init__(*snake_case_  ,		**snake_case_					)






