

def       __snake_case						( __UpperCamelCase	: str    ):



      """simple docstring"""

      return credit_card_number.startswith(("34", "35", "37", "4", "5", "6")    )

def       __snake_case						( __UpperCamelCase	: str    ):



      """simple docstring"""

      A_						  =				credit_card_number
      A_						  =				0
      A_						  =				len(__UpperCamelCase    ) - 2
      for i in range(__UpperCamelCase				,-1				,-2    ):
            #  double the value of every second digit
            A_						  =				int(cc_number[i]    )
            digit *= 2
            # If doubling of a number results in a two digit number
            # i.e greater than 9(e.g., 6 × 2 = 12),
            # then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
            # to get a single digit number.
            if digit > 9:
                  digit %= 10
                  digit += 1
            A_						  =				cc_number[:i] + str(__UpperCamelCase    ) + cc_number[i + 1 :]
            total += digit

      # Sum up the remaining digits
      for i in range(len(__UpperCamelCase    ) - 1				,-1				,-2    ):
            total += int(cc_number[i]    )

      return total % 10 == 0

def       __snake_case						( __UpperCamelCase	: str    ):



      """simple docstring"""

      A_						  =				f'''{credit_card_number} is an invalid credit card number because'''
      if not credit_card_number.isdigit():
            print(f'''{error_message} it has nonnumerical characters.'''    )
            return False

      if not 13 <= len(__UpperCamelCase    ) <= 16:
            print(f'''{error_message} of its length.'''    )
            return False

      if not validate_initial_digits(__UpperCamelCase    ):
            print(f'''{error_message} of its first two digits.'''    )
            return False

      if not luhn_validation(__UpperCamelCase    ):
            print(f'''{error_message} it fails the Luhn check.'''    )
            return False

      print(f'''{credit_card_number} is a valid credit card number.'''    )
      return True


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      validate_credit_card_number('4111111111111111')
      validate_credit_card_number('32323')



import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[int]:
    # Initialise PyTorch model
    _lowercase			:				Optional[int] 						=						TaConfig.from_json_file(lowerCamelCase_					)
    print(F'''Building PyTorch model from configuration: {config}'''					)
    _lowercase			:				Union[str, Any] 						=						TaForConditionalGeneration(lowerCamelCase_					)

    # Load weights from tf checkpoint
    load_tf_weights_in_ta(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)

    # Save pytorch-model
    print(F'''Save PyTorch model to {pytorch_dump_path}'''					)
    model.save_pretrained(lowerCamelCase_					)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: Optional[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(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained T5 model. \nThis 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			: Any				   =					parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


