





"""simple docstring"""





import argparse

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


logging.set_verbosity_info()
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Optional[int]:





  """simple docstring"""


  lowerCAmelCase__     :int											=				TaConfig.from_json_file(_SCREAMING_SNAKE_CASE     )
  print(F"Building PyTorch model from configuration: {config}"     )
  lowerCAmelCase__     :Union[str, Any]											=				TaForConditionalGeneration(_SCREAMING_SNAKE_CASE     )

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

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


if __name__ == "__main__":
  __A											=						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."""
  )
  __A											=						parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)







"""simple docstring"""





def 					__A  (_SCREAMING_SNAKE_CASE     )   ->str:





  """simple docstring"""


  lowerCAmelCase__     :List[Any]											=				int(_SCREAMING_SNAKE_CASE     )
  if decimal in (0, 1):  # Exit cases for the recursion
    return str(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__				,							lowerCAmelCase__     :int											=				divmod(_SCREAMING_SNAKE_CASE	,  2     )
  return binary_recursive(_SCREAMING_SNAKE_CASE     ) + str(_SCREAMING_SNAKE_CASE     )
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->str:





  """simple docstring"""


  lowerCAmelCase__     :str											=				str(_SCREAMING_SNAKE_CASE     ).strip()
  if not number:
    raise ValueError('No input value was provided'     )
  lowerCAmelCase__     :Dict											=				'-' if number.startswith('-'     ) else ''
  lowerCAmelCase__     :int											=				number.lstrip('-'     )
  if not number.isnumeric():
    raise ValueError('Input value is not an integer'     )
  return F"{negative}0b{binary_recursive(int(_SCREAMING_SNAKE_CASE     )     )}"


if __name__ == "__main__":
  from doctest import testmod

  testmod()
