
lowerCamelCase_				:							Optional[Any]       =    {hex(i)[2:]: i for i in range(16)}  # Use [:2] to strip off the leading '0x'
def        __lowercase(							__snake_case				:		str					)       ->   int:
       __snake_case						       =   hex_string.strip().lower()
       if not hex_string:
              raise ValueError('Empty string was passed to the function'					)
       __snake_case						       =   hex_string[0] == '-'
       if is_negative:
              __snake_case						       =   hex_string[1:]
       if not all(char in hex_table for char in hex_string					):
              raise ValueError('Non-hexadecimal value was passed to the function'					)
       __snake_case						       =   0
       for char in hex_string:
              __snake_case						       =   16 * decimal_number + hex_table[char]
       return -decimal_number if is_negative else decimal_number


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

 testmod()


import argparse

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


logging.set_verbosity_info()
def        __lowercase(							__snake_case				:		Dict    ,__snake_case				:		List[str]    ,__snake_case				:		List[Any]					)       ->   str:
       __snake_case						       =   ConvBertConfig.from_json_file(__snake_case					)
       __snake_case						       =   ConvBertModel(__snake_case					)

       __snake_case						       =   load_tf_weights_in_convbert(__snake_case    ,__snake_case    ,__snake_case					)
       model.save_pretrained(__snake_case					)

       __snake_case						       =   TFConvBertModel.from_pretrained(__snake_case    ,from_pt=__snake_case					)
       tf_model.save_pretrained(__snake_case					)


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