






'''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 (			A			:	str	,   A			:	List[Any]	,   A			:	List[str]						)  -> Optional[int]:
			UpperCAmelCase_     :   Dict       =   ConvBertConfig.from_json_file(A						)
			UpperCAmelCase_     :   List[Any]       =   ConvBertModel(A						)

			UpperCAmelCase_     :   List[str]       =   load_tf_weights_in_convbert(A	,   A	,   A						)
			model.save_pretrained(A						)

			UpperCAmelCase_     :   Optional[int]       =   TFConvBertModel.from_pretrained(A	,   from_pt=A						)
			tf_model.save_pretrained(A						)


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










'''simple docstring'''





def __UpperCAmelCase (			)  -> Optional[Any]:
			UpperCAmelCase_     :   Union[str, Any]       =   1

			while True:
						if (
						    sorted(str(A						)						)
						    == sorted(str(2 * i						)						)
						    == sorted(str(3 * i						)						)
						    == sorted(str(4 * i						)						)
						    == sorted(str(5 * i						)						)
						    == sorted(str(6 * i						)						)
						):
									return i

						i += 1


if __name__ == "__main__":
						print(solution())


