

def    A_  (							a  ):

      """simple docstring"""



      if any(not isinstance(a					,   a  ) or x < 0 for x in sequence  ):
            raise TypeError('Sequence must be list of non-negative integers'  )
      for _ in range(len(a  )  ):
            for i, (rod_upper, rod_lower) in enumerate(zip(a					,   sequence[1:]  )  ):
                  if rod_upper > rod_lower:
                        sequence[i] -= rod_upper - rod_lower
                        sequence[i + 1] += rod_upper - rod_lower
      return sequence


if __name__ == "__main__":
    assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
    assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]









import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()

def    A_  (							a					,   a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  int           =     TaConfig.from_json_file(a  )
      print(f"Building PyTorch model from configuration: {config}"  )
      SCREAMING_SNAKE_CASE_				:  Tuple           =     TaForConditionalGeneration(a  )

      # Load weights from tf checkpoint
      load_tf_weights_in_ta(a					,   a					,   a  )

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


if __name__ == "__main__":
    lowerCAmelCase     :   int						     =  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
        ),
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    lowerCAmelCase     :   List[str]						     =  parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






