import argparse

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


logging.set_verbosity_info()






def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			):
       # Initialise PyTorch model
       lowerCAmelCase__  			=      BertConfig.from_pretrained(
           'bert-large-cased'			,						vocab_size=lowerCAmelCase__			,						max_position_embeddings=5_1_2			,						is_decoder=lowerCAmelCase__			,						add_cross_attention=lowerCAmelCase__			,						)
       lowerCAmelCase__  			=      bert_config.to_dict()
       del bert_config_dict["type_vocab_size"]
       lowerCAmelCase__  			=      BertGenerationConfig(**lowerCAmelCase__			)
       if is_encoder:
              lowerCAmelCase__  			=      BertGenerationEncoder(lowerCAmelCase__			)
       else:
              lowerCAmelCase__  			=      BertGenerationDecoder(lowerCAmelCase__			)
       print(F"""Building PyTorch model from configuration: {config}"""			)

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

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


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=50358, 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,
    )

def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			):
       if target == 0:
              answer.append(path.copy()			)
       else:
              for index in range(lowerCAmelCase__			,						len(lowerCAmelCase__			)			):
                     if target >= candidates[index]:
                            path.append(candidates[index]			)
                            backtrack(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						target - candidates[index]			,						lowerCAmelCase__			)
                            path.pop(len(lowerCAmelCase__			) - 1			)






def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			):
       lowerCAmelCase__  			=      []  # type: list[int]
       lowerCAmelCase__  			=      []  # type: list[int]
       backtrack(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						0			)
       return answer






def    __lowerCamelCase	(		):
       print(combination_sum([-8, 2.3, 0]			,						1			)			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()
