



import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case  ,			__snake_case  ,			__snake_case  ,			__snake_case							)				->	Union[str, Any]:
  # Initialise PyTorch model
  _A          =      BertConfig.from_pretrained(
      '''bert-large-cased'''  ,			vocab_size=__snake_case  ,			max_position_embeddings=512  ,			is_decoder=__snake_case  ,			add_cross_attention=__snake_case  ,			)
  _A          =      bert_config.to_dict()
  del bert_config_dict["type_vocab_size"]
  _A          =      BertGenerationConfig(**__snake_case							)
  if is_encoder:
    _A          =      BertGenerationEncoder(__snake_case							)
  else:
    _A          =      BertGenerationDecoder(__snake_case							)
  print(F'''Building PyTorch model from configuration: {config}'''							)

  # Load weights from tf checkpoint
  load_tf_weights_in_bert_generation(
      __snake_case  ,			__snake_case  ,			model_class='''bert'''  ,			is_encoder_named_decoder=__snake_case  ,			is_encoder=__snake_case  ,			)

  # Save pytorch-model
  print(F'''Save PyTorch model and config to {pytorch_dump_path}'''							)
  model.save_pretrained(__snake_case							)


if __name__ == "__main__":
    __lowerCamelCase								=				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--tf_hub_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--is_encoder_named_decoder""",
        action="""store_true""",
        help="""If decoder has to be renamed to encoder in PyTorch model.""",
    )
    parser.add_argument("""--is_encoder""", action="""store_true""", help="""If model is an encoder.""")
    parser.add_argument("""--vocab_size""", default=5_0_3_5_8, type=int, help="""Vocab size of model""")
    __lowerCamelCase								=				parser.parse_args()
    convert_tf_checkpoint_to_pytorch(
        args.tf_hub_path,
        args.pytorch_dump_path,
        args.is_encoder_named_decoder,
        args.vocab_size,
        is_encoder=args.is_encoder,
    )




from __future__ import annotations




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case  ,			__snake_case  ,			__snake_case							)				->	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(__snake_case  ,			__snake_case  ,			__snake_case  ,			counter - 1							)
  else:
    _A          =      capacity - weights[counter - 1]
    _A          =      values[counter - 1] + knapsack(
        __snake_case  ,			__snake_case  ,			__snake_case  ,			counter - 1							)
    _A          =      knapsack(__snake_case  ,			__snake_case  ,			__snake_case  ,			counter - 1							)
    return max(__snake_case  ,			__snake_case							)


if __name__ == "__main__":
    import doctest

    doctest.testmod()