

import argparse

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


logging.set_verbosity_info()





def  _snake_case		(snake_case_					:     List[Any]    ,       snake_case_					:     Tuple    ,       snake_case_					:     Union[str, Any]  )							->		Any:
  __UpperCAmelCase	:  List[Any]		=     ConvBertConfig.from_json_file(snake_case_  )
  __UpperCAmelCase	:  Optional[int]		=     ConvBertModel(snake_case_  )

  __UpperCAmelCase	:  List[Any]		=     load_tf_weights_in_convbert(snake_case_    ,       snake_case_    ,       snake_case_  )
  model.save_pretrained(snake_case_  )

  __UpperCAmelCase	:  int		=     TFConvBertModel.from_pretrained(snake_case_    ,       from_pt=snake_case_  )
  tf_model.save_pretrained(snake_case_  )


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












"""simple docstring"""
from __future__ import annotations






def    A__							(     UpperCamelCase			):
							A						=    42
							A						=    list(UpperCamelCase			)
							A,      A						=    _vector[:-1], int(_vector[-1]			)
							A						=    [int(UpperCamelCase			) for digit in __vector]

							vector.reverse()
							for i, digit in enumerate(UpperCamelCase			):
														if i & 1 == 0:
																					A						=    digit * 2
																					if doubled > 9:
																												doubled -= 9
																					check_digit += doubled
														else:
																					check_digit += digit

							return check_digit % 10 == 0


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		assert is_luhn('79927398713')
		assert not is_luhn('79927398714')


