





import argparse

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


logging.set_verbosity_info()




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						Union[str, Any] ,     snake_case_		:						Union[str, Any] ,     snake_case_		:						List[Any]						):
     __magic_name__				=       ConvBertConfig.from_json_file(snake_case_						)
     __magic_name__				=       ConvBertModel(snake_case_						)

     __magic_name__				=       load_tf_weights_in_convbert(snake_case_ ,     snake_case_ ,     snake_case_						)
     model.save_pretrained(snake_case_						)

     __magic_name__				=       TFConvBertModel.from_pretrained(snake_case_ ,     from_pt=snake_case_						)
     tf_model.save_pretrained(snake_case_						)


if __name__ == "__main__":
    a_    :				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(
        '--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.'
    )
    a_    :				str					=			parser.parse_args()
    convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)






def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						int						):
     if not isinstance(snake_case_ ,     snake_case_						):
          __magic_name__				=       f'Input value of [number={number}] must be an integer'
          raise TypeError(snake_case_						)
     if number < 0 or number % 2 == 1:
          return False
     __magic_name__				=       int(number**0.5						)
     return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()