





'''simple docstring'''







import argparse

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


logging.set_verbosity_info()





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	):
    # Initialise PyTorch model
    UpperCamelCase__      =			BertConfig.from_pretrained(
        "bert-large-cased"					,  vocab_size=lowerCAmelCase_					,  max_position_embeddings=5_12					,  is_decoder=lowerCAmelCase_					,  add_cross_attention=lowerCAmelCase_					,  )
    UpperCamelCase__      =			bert_config.to_dict()
    del bert_config_dict["type_vocab_size"]
    UpperCamelCase__      =			BertGenerationConfig(**lowerCAmelCase_	)
    if is_encoder:
        UpperCamelCase__      =			BertGenerationEncoder(lowerCAmelCase_	)
    else:
        UpperCamelCase__      =			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__":
      lowercase__     :  Tuple    					=			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_03_58, type=int, help='Vocab size of model')
      lowercase__     :  Dict    					=			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,
      )









'''simple docstring'''







def 						snake_case__ (lowerCAmelCase_	):
    for i, _ in enumerate(lowerCAmelCase_	):
        if (i % 2 == 1) == (nums[i - 1] > nums[i]):
            UpperCamelCase__    ,  UpperCamelCase__      =			nums[i], nums[i - 1]

    return nums


if __name__ == "__main__":
      print('Enter the array elements:')
      lowercase__     :  Optional[int]    					=			list(map(int, input().split()))
      print('The unsorted array is:')
      print(array)
      print('Array after Wiggle sort:')
      print(wiggle_sort(array))




