


"""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 _A		(		lowercase			,       lowercase			,       lowercase						):







    """simple docstring"""
    a											=ConvBertConfig.from_json_file(lowercase						)
    a											=ConvBertModel(lowercase						)

    a											=load_tf_weights_in_convbert(lowercase			,       lowercase			,       lowercase						)
    model.save_pretrained(lowercase						)

    a											=TFConvBertModel.from_pretrained(lowercase			,       from_pt=lowercase						)
    tf_model.save_pretrained(lowercase						)


if __name__ == "__main__":
       lowerCamelCase_  :  Dict     		=    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."""
       )
       lowerCamelCase_  :  List[str]     		=    parser.parse_args()
       convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)



"""simple docstring"""







def _A		(		lowercase						):







    """simple docstring"""
    a											=[]
    a											=1

    for i in range(len(lowercase						)						):
        if i + 1 < len(lowercase						) and text[i] == text[i + 1]:
            count += 1
        else:
            encoded.append((text[i], count)						)
            a											=1

    return encoded
def _A		(		lowercase						):







    """simple docstring"""
    return "".join(char * length for char, length in encoded						)


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

       testmod(name="""run_length_encode""", verbose=True)
       testmod(name="""run_length_decode""", verbose=True)