code: 
  
  from __future__ import annotations
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> 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(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      counter - 1				)
      else:
          lowerCAmelCase    			=      capacity - weights[counter - 1]
          lowerCAmelCase    			=      values[counter - 1] + knapsack(
              _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      counter - 1				)
          lowerCAmelCase    			=      knapsack(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      counter - 1				)
          return max(_UpperCAmelCase,      _UpperCAmelCase				)
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
  
  
  
  
  
  
style_context: 
  
  __A   :     List[Any]      =			'''
  # Installation de Transformers
  ! pip install transformers datasets
  # Pour installer à partir du code source au lieu de la dernière version, commentez la commande ci-dessus et décommentez la suivante.
  # ! pip install git+https://github.com/huggingface/transformers.git
  '''
  
  __A   :     Dict      =			[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
  __A   :     Tuple      =			{
      '''{processor_class}''': '''FakeProcessorClass''',
      '''{model_class}''': '''FakeModelClass''',
      '''{object_class}''': '''FakeObjectClass''',
  }
  
  
  
  
  
  