
def 		lowercase_    (     A__	)							->							bool:
    """simple docstring"""

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



def 		lowercase_    (     A__	)							->							bool:
    """simple docstring"""

    snake_case				  =      credit_card_number
    snake_case				  =      0
    snake_case				  =      len(A__	) - 2
    for i in range(A__				,	-1				,	-2	):
        #  double the value of every second digit
        snake_case				  =      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
        snake_case				  =      cc_number[:i] + str(A__	) + cc_number[i + 1 :]
        total += digit

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

    return total % 10 == 0



def 		lowercase_    (     A__	)							->							bool:
    """simple docstring"""

    snake_case				  =      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(A__	) <= 16:
        print(F'{error_message} of its length.'	)
        return False

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

    if not luhn_validation(A__	):
        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 unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer







@require_torch
@require_sentencepiece
@require_tokenizers
class   lowerCamelCase	(						unittest.TestCase						):





     @slow
     def 	UpperCAmelCase(self   :					List[str]     )		->		Optional[int]:
         snake_case				  =      AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"       ,					return_dict=_A     ).to(_A     )
         snake_case				  =      AutoTokenizer.from_pretrained("google/mt5-small"     )

         snake_case				  =      tokenizer("Hello there"       ,					return_tensors="pt"     ).input_ids
         snake_case				  =      tokenizer("Hi I am"       ,					return_tensors="pt"     ).input_ids

         snake_case				  =      model(input_ids.to(_A     )       ,					labels=labels.to(_A     )     ).loss
         snake_case				  =      -(labels.shape[-1] * loss.item())

         snake_case				  =      -84.91_27
         self.assertTrue(abs(mtf_score - EXPECTED_SCORE     ) < 1E-4     )

