

from __future__ import annotations






def    __lowercase (       _A  ,      _A  ,      _A  ,      _A     )       ->  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(_A  ,      _A  ,      _A  ,      counter - 1     )
      else:
            SCREAMING_SNAKE_CASE	: List[Any]					       =				capacity - weights[counter - 1]
            SCREAMING_SNAKE_CASE	: str					       =				values[counter - 1] + knapsack(
                _A  ,      _A  ,      _A  ,      counter - 1     )
            SCREAMING_SNAKE_CASE	: Tuple					       =				knapsack(_A  ,      _A  ,      _A  ,      counter - 1     )
            return max(_A  ,      _A     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()







UpperCAmelCase__	:       Union[str, 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
"""

UpperCAmelCase__	:       List[Any]			      =      [{"""type""": """code""", """content""": INSTALL_CONTENT}]
UpperCAmelCase__	:       List[Any]			      =      {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}




