import argparse

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


logging.set_verbosity_info()




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A,						__A							)  ->       List[Any]:







      '''simple docstring'''


      UpperCAmelCase__			=						BertConfig.from_pretrained(
          "bert-large-cased",						vocab_size=__A,						max_position_embeddings=512,						is_decoder=__A,						add_cross_attention=__A,						)
      UpperCAmelCase__			=						bert_config.to_dict()
      del bert_config_dict["type_vocab_size"]
      UpperCAmelCase__			=						BertGenerationConfig(**__A							)
      if is_encoder:
            UpperCAmelCase__			=						BertGenerationEncoder(__A							)
      else:
            UpperCAmelCase__			=						BertGenerationDecoder(__A							)
      print(f"""Building PyTorch model from configuration: {config}"""							)

      # Load weights from tf checkpoint
      load_tf_weights_in_bert_generation(
          __A,						__A,						model_class="bert",						is_encoder_named_decoder=__A,						is_encoder=__A,						)

      # Save pytorch-model
      print(f"""Save PyTorch model and config to {pytorch_dump_path}"""							)
      model.save_pretrained(__A							)


if __name__ == "__main__":
      UpperCamelCase__       							=      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')
      UpperCamelCase__       							=      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,
      )




UpperCamelCase__       							=      [
    [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
]




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A							)  ->       bool:







      '''simple docstring'''


      return 0 <= row < rows and 0 <= col < cols




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A							)  ->       int:







      '''simple docstring'''


      UpperCAmelCase__			=						len(__A							)
      UpperCAmelCase__			=						len(mat[0]							)
      if is_safe(__A,						__A,						__A,						__A							) and (row, col) not in seen and mat[row][col] == 1:
            seen.add((row, col)							)
            return (
                1
                + depth_first_search(row + 1,						__A,						__A,						__A							)
                + depth_first_search(row - 1,						__A,						__A,						__A							)
                + depth_first_search(__A,						col + 1,						__A,						__A							)
                + depth_first_search(__A,						col - 1,						__A,						__A							)
            )
      else:
            return 0




def      lowerCAmelCase_				(  __A							)  ->       int:







      '''simple docstring'''


      UpperCAmelCase__			=						set()

      UpperCAmelCase__			=						0
      for row, line in enumerate(__A							):
            for col, item in enumerate(__A							):
                  if item == 1 and (row, col) not in seen:
                        # Maximizing the area
                        UpperCAmelCase__			=						max(__A,						depth_first_search(__A,						__A,						__A,						__A							)							)
      return max_area


if __name__ == "__main__":
      import doctest

      print(find_max_area(matrix))  # Output -> 6
      doctest.testmod()



