'''simple docstring'''




import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()





def      _UpperCAmelCase    (							_UpperCamelCase    :	Any,   _UpperCamelCase    :	str,   _UpperCamelCase    :	Any   )	->      Union[str, Any]:
    A_     =   ConvBertConfig.from_json_file(_UpperCamelCase   )
    A_     =   ConvBertModel(_UpperCamelCase   )

    A_     =   load_tf_weights_in_convbert(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )
    model.save_pretrained(_UpperCamelCase   )

    A_     =   TFConvBertModel.from_pretrained(_UpperCamelCase,   from_pt=_UpperCamelCase   )
    tf_model.save_pretrained(_UpperCamelCase   )


if __name__ == "__main__":
  __snake_case   :      Union[str, Any]						    =  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(
      '--convbert_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained ConvBERT model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  __snake_case   :      int						    =  parser.parse_args()
  convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)







'''simple docstring'''




from scipy.constants import g





def      _UpperCAmelCase    (							_UpperCamelCase    :	float,   _UpperCamelCase    :	float   )	->      float:
    if mass < 0:
        # handling of negative values of mass
        raise ValueError('''The mass of a body cannot be negative'''   )
    if height < 0:
        # handling of negative values of height
        raise ValueError('''The height above the ground cannot be negative'''   )
    return mass * g * height


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

  testmod(name='potential_energy')






